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J. Hemalatha, R., Dr V. Vijaybaskar, A. Josephin Arockia Dhivya, and . "Early detection of joint abnormalities from ultrasound images." International Journal of Engineering & Technology 7, no. 2.25 (2018): 105. http://dx.doi.org/10.14419/ijet.v7i2.25.16569.
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Musculoskeletal ultrasound is effective for the early detection of joint abnormalities like erosion, effusion, synovitis and inflammation. Computer software is developed for segmentation of joint ultrasound image to diagnose the defect. The objective of developing this paper is to achieve early diagnosis of joint disorders by segmentation of ultrasound image with different algorithms. Ultrasound machine with high resolution probe can be used for development & findings of joints by the orthopaedician, rheumatologist and sports physician. These find-ings are done by processing the ultrasound images of patient joint using modern image processing techniques. Therefore algorithms has been designed and developed for analysis of medical images that is musculo ultrasound image based on optimization approach, using genet-ic algorithm and PSO algorithm. To improve the better quality of the image many improvisation techniques have been introduced. Hence, these algorithms perform better segmentation and identification of joint abnormalities. The analysis of ultrasound image is directly based on image segmentation steps, pre-processing, filtering, feature extraction and analysis of these extracted features by finding the output using different optimization techniques. In proposed method, efforts have been made to exhibit the procedure for finding and segmenting the mus-culoskeletal images of abnormal joints. The present approaches are segmentation operation on ultrasound images by applying genetic and PSO algorithm. The comparison between these algorithms is done, such that the algorithm itself analyses the whole image and perform the segmentation and detection of abnormalities perfectly
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Kwak, Deawon, Jiwoo Choi, and Sungjin Lee. "Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition." Sensors 23, no. 4 (2023): 2307. http://dx.doi.org/10.3390/s23042307.
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This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3), and related loss functions (binary cross entropy, dice Loss, Tversky loss), and data augmentation. As a result of evaluations through the presented methods, when using filter-based data augmentation, ResNet50 showed the best performance in image classification, and UNet showed the best performance in both X-ray image and ultrasound image as image segmentation. When applying the proposed image recognition strategies for the maximal diagnosis accuracy in each medical image data, the accuracy can be improved by 33.3% in image segmentation in X-ray images, 29.9% in image segmentation in ultrasound images, and 22.8% in image classification in histopathology images.
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Bao, Junxiao, Cuilin Bei, Xiang Zheng, and Jinli Wang. "Deep Learning Algorithm in Biomedical Engineering in Intelligent Automatic Processing and Analysis of Sports Images." Wireless Communications and Mobile Computing 2022 (July 30, 2022): 1–10. http://dx.doi.org/10.1155/2022/3196491.
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In order to improve the detection and identification ability of sports injury ultrasound medicine, a segmentation method of sports injury ultrasound medical image based on local features is proposed, and the research on the sports injury ultrasound medical detection and identification ability is carried out. Methods of the sports injury ultrasound medical image segmentation model are established; the sports injury ultrasound medical image information is enhanced by using the sports skeletal muscle block matching technology; the image features are extracted; and the characteristics of sports injury ultrasound medical images are analyzed by CT bright spot feature transmission. In detail, combined with the deep convolutional neural network training method, the extracted sports injury points are automatically detected for sports injury ultrasound medical images, and the sports injury ultrasound medical image segmentation is realized. The simulation results show that this method has high accuracy for sports injury ultrasound medical image segmentation, the error value can be controlled below 0.103, and finally, the effect of zero error is achieved. It is confirmed that the method proposed in this paper has high resolution and accuracy for sports injury point detection and has strong practical application ability.
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Shao, Liping, Zubang Zhou, Hongmei Wu, Jinrong Ni, and Shulan Li. "Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis." Journal of Healthcare Engineering 2021 (April 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/5597591.
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Different segmentation of lung nodules using the same segmentation algorithm can easily lead to excessive segmentation errors. Therefore, it is necessary to design an effective segmentation algorithm to improve image segmentation accuracy. Based on the hidden Markov model, this study processed the ultrasound images of pulmonary nodules to improve their diagnostic results. At the same time, this study was combined with the ultrasound image of lung nodules to process the ultrasound images. In addition, this study combines the convex hull algorithm for image processing, uses the improved vector method to repair, improves image recognizability, establishes a reliable feature extraction algorithm, and establishes a comprehensive diagnostic model. Finally, this study designed the test for performance analysis. Through experimental research, it can be seen that the model constructed in this study has certain clinical effects and can provide theoretical reference for subsequent related research.
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Sree, S. Jayanthi, and C. Vasanthanayaki. "Ultrasound Fetal Image Segmentation Techniques: A Review." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 1 (2018): 52–60. http://dx.doi.org/10.2174/1573405613666170622115527.
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Background: This paper reviews segmentation techniques for 2D ultrasound fetal images. Fetal anatomy measurements derived from the segmentation results are used to monitor the growth of the fetus. </P><P> Discussion: The segmentation of fetal ultrasound images is a difficult task due to inherent artifacts and degradation of image quality with gestational age. There are segmentation techniques for particular biological structures such as head, stomach, and femur. The whole fetal segmentation algorithms are only very few. Conclusion: This paper presents a review of these segmentation techniques and the metrics used to evaluate them are summarized.
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Wang, Yuxin, Jialiang Zhang, Jiangning Han, et al. "Ovarian Ultrasound Image Segmentation Algorithm with Fused Multi-Scale Features." Critical Reviews in Biomedical Engineering 53, no. 1 (2025): 47–57. http://dx.doi.org/10.1615/critrevbiomedeng.v53.i1.40.
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Ultrasound imaging technology plays a vital role in medical imaging. Ovarian ultrasound image segmentation is challenging due to the wide variation in lesion sizes caused by the cancer detection period and individual differences, as well as the noise from reflected wave interference. To address these challenges, we propose an innovative algorithm for ovarian ultrasound image segmentation that incorporates multi-scale features. This algorithm effectively processes image data with varying scales. By introducing a skip connection structure, the shallow image features are preserved. Additionally, in the feature fusion module, feature maps extracted from the backbone network are integrated layer by layer, enhancing the model's ability to parse multi-scale features. The proposed algorithm was tested on ovarian ultrasound images that had undergone noise reduction using different filtering methods. When compared to mainstream segmentation algorithms, our model achieved improvements in mIoU, mAcc, and aAcc metrics by 2.02, 1.09, and 0.34&#37;, respectively. Overall, the algorithm outperformed the comparison methods, offering a new solution for ovarian ultrasound image segmentation.
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Sun, Jingmeng, and Yifei Liu. "Segmentation for Human Motion Injury Ultrasound Medical Images Using Deep Feature Fusion." Mathematical Problems in Engineering 2022 (August 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/4825720.
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Image processing technology assists physicians in the analysis of athletes’ human motion injuries, not only to improve the accuracy of athletes’ injury detection but also to improve the localization and recognition of injury locations. It is important to accurately segment human motion injury ultrasound medical images. To address many problems such as poor effect of traditional ultrasonic medical image segmentation algorithm for a sports injury. Therefore, we propose a segmentation algorithm for human motion injury ultrasound medical images using deep feature fusion. First, the accurate estimated value of human posture is extracted and combined with image texture features and image gray value as the target feature value of the ultrasonic medical image of human motion injury. Second, the image features are deeply fused by an adaptive fusion algorithm to enhance the image resolution. Finally, the best segmentation value of the image is obtained by the trained support vector machine to realize the accurate segmentation of human motion injury ultrasonic medical image. The results show that the average accuracy of the posture accurate estimation of the proposed algorithm is 95.97%; the segmentation time of the human motion injury ultrasound medical image of the proposed algorithm is below 150 ms; and the convergence of the algorithm is completed when the number of iterations is 3. The maximum segmentation error rate is 2.68%. The image segmentation effect is consistent with the ideal target segmentation effect. The proposed algorithm has important application value in the field of ultrasonic medical diagnosis of sports injury.
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Suri, Jasjit, Yujun Guo, Cara Coad, Tim Danielson, Idris Elbakri, and Roman Janer. "Image Quality Assessment via Segmentation of Breast Lesion in X-ray and Ultrasound Phantom Images from Fischer's Full Field Digital Mammography and Ultrasound (FFDMUS) System." Technology in Cancer Research & Treatment 4, no. 1 (2005): 83–92. http://dx.doi.org/10.1177/153303460500400111.
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Fischer has been developing a fused full-field digital mammography and ultrasound (FFDMUS) system funded by the National Institute of Health (NIH). In FFDMUS, two sets of acquisitions are performed: 2-D X-ray and 3-D ultrasound. The segmentation of acquired lesions in phantom images is important: (i) to assess the image quality of X-ray and ultrasound images; (ii) to register multi-modality images; and (iii) to establish an automatic lesion detection methodology to assist the radiologist. In this paper we developed lesion segmentation strategies for ultrasound and X-ray images acquired using FFDMUS. For ultrasound lesion segmentation, a signal-to-noise (SNR)-based method was adapted. For X-ray segmentation, we used gradient vector flow (GVF)-based deformable model. The performance of these segmentation algorithms was evaluated. We also performed partial volume correction (PVC) analysis on the segmentation of ultrasound images. For X-ray lesion segmentation, we also studied the effect of PDE smoothing on GVF's ability to segment the lesion. We conclude that ultrasound image qualities from FFDMUS and Hand-Held ultrasound (HHUS) are comparable. The mean percentage error with PVC was 4.56% (4.31%) and 6.63% (5.89%) for 5 mm lesion and 3 mm lesion respectively. The mean average error from the segmented X-ray images with PDE yielded an average error of 9.61%. We also tested our program on synthetic datasets. The system was developed for Linux workstation using C/C++.
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Xiao, Xiaolong, Jianfeng Zhang, Yuan Shao, et al. "Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges." Sensors 25, no. 8 (2025): 2361. https://doi.org/10.3390/s25082361.
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The intricate imaging structures, artifacts, and noise present in ultrasound images and videos pose significant challenges for accurate segmentation. Deep learning has recently emerged as a prominent field, playing a crucial role in medical image processing. This paper reviews ultrasound image and video segmentation methods based on deep learning techniques, summarizing the latest developments in this field, such as diffusion and segment anything models as well as classical methods. These methods are classified into four main categories based on the characteristics of the segmentation methods. Each category is outlined and evaluated in the corresponding section. We provide a comprehensive overview of deep learning-based ultrasound image segmentation methods, evaluation metrics, and common ultrasound datasets, hoping to explain the advantages and disadvantages of each method, summarize its achievements, and discuss challenges and future trends.
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Wu, Shibin, Shaode Yu, Ling Zhuang, et al. "Automatic Segmentation of Ultrasound Tomography Image." BioMed Research International 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/2059036.
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Ultrasound tomography (UST) image segmentation is fundamental in breast density estimation, medicine response analysis, and anatomical change quantification. Existing methods are time consuming and require massive manual interaction. To address these issues, an automatic algorithm based on GrabCut (AUGC) is proposed in this paper. The presented method designs automated GrabCut initialization for incomplete labeling and is sped up with multicore parallel programming. To verify performance, AUGC is applied to segment thirty-two in vivo UST volumetric images. The performance of AUGC is validated with breast overlapping metrics (Dice coefficient (D), Jaccard (J), and False positive (FP)) and time cost (TC). Furthermore, AUGC is compared to other methods, including Confidence Connected Region Growing (CCRG), watershed, and Active Contour based Curve Delineation (ACCD). Experimental results indicate that AUGC achieves the highest accuracy (D=0.9275 and J=0.8660 and FP=0.0077) and takes on average about 4 seconds to process a volumetric image. It was said that AUGC benefits large-scale studies by using UST images for breast cancer screening and pathological quantification.
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Noble, J. A., and D. Boukerroui. "Ultrasound image segmentation: a survey." IEEE Transactions on Medical Imaging 25, no. 8 (2006): 987–1010. http://dx.doi.org/10.1109/tmi.2006.877092.
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Cai, Lina, Qingkai Li, Junhua Zhang, Zhenghua Zhang, Rui Yang, and Lun Zhang. "Ultrasound image segmentation based on Transformer and U-Net with joint loss." PeerJ Computer Science 9 (October 20, 2023): e1638. http://dx.doi.org/10.7717/peerj-cs.1638.
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Background Ultrasound image segmentation is challenging due to the low signal-to-noise ratio and poor quality of ultrasound images. With deep learning advancements, convolutional neural networks (CNNs) have been widely used for ultrasound image segmentation. However, due to the intrinsic locality of convolutional operations and the varying shapes of segmentation objects, segmentation methods based on CNNs still face challenges with accuracy and generalization. In addition, Transformer is a network architecture with self-attention mechanisms that performs well in the field of computer vision. Based on the characteristics of Transformer and CNNs, we propose a hybrid architecture based on Transformer and U-Net with joint loss for ultrasound image segmentation, referred to as TU-Net. Methods TU-Net is based on the encoder-decoder architecture and includes encoder, parallel attention mechanism and decoder modules. The encoder module is responsible for reducing dimensions and capturing different levels of feature information from ultrasound images; the parallel attention mechanism is responsible for capturing global and multiscale local feature information; and the decoder module is responsible for gradually recovering dimensions and delineating the boundaries of the segmentation target. Additionally, we adopt joint loss to optimize learning and improve segmentation accuracy. We use experiments on datasets of two types of ultrasound images to verify the proposed architecture. We use the Dice scores, precision, recall, Hausdorff distance (HD) and average symmetric surface distance (ASD) as evaluation metrics for segmentation performance. Results For the brachia plexus and fetal head ultrasound image datasets, TU-Net achieves mean Dice scores of 79.59% and 97.94%; precisions of 81.25% and 98.18%; recalls of 80.19% and 97.72%; HDs (mm) of 12.44 and 6.93; and ASDs (mm) of 4.29 and 2.97, respectively. Compared with those of the other six segmentation algorithms, the mean values of TU-Net increased by approximately 3.41%, 2.62%, 3.74%, 36.40% and 31.96% for the Dice score, precision, recall, HD and ASD, respectively.
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Saeed, Jwan N. "A SURVEY OF ULTRASONOGRAPHY BREAST CANCER IMAGE SEGMENTATION TECHNIQUES." Academic Journal of Nawroz University 9, no. 1 (2020): 1. http://dx.doi.org/10.25007/ajnu.v9n1a523.
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The most common cause of death among women globally is breast cancer. One of the key strategies to reduce mortality associated with breast cancer is to develop effective early detection techniques. The segmentation of breast ultrasound (BUS) image in Computer-Aided Diagnosis (CAD) systems is critical and challenging. Image segmentation aims to represent the image in a simplified and more meaningful way while retaining crucial features for easier analysis. However, in the field of image processing, image segmentation is a tough task particularly in ultrasound (US) images due to challenges associated with their nature. This paper presents a survey on several techniques of ultrasonography images segmentation including threshold based, region based, watershed, active contour and learning based techniques, their merits, and demerits. This can provide significant insights for CAD developers or researchers to advance this field.
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Orlando, Nathan, Igor Gyacskov, Derek J. Gillies, et al. "Effect of dataset size, image quality, and image type on deep learning-based automatic prostate segmentation in 3D ultrasound." Physics in Medicine & Biology 67, no. 7 (2022): 074002. http://dx.doi.org/10.1088/1361-6560/ac5a93.
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Abstract Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image quality grading method. The complete training dataset of 6761 2D images, resliced from 206 3D TRUS volumes acquired using end-fire and side-fire acquisition methods, was split to train two separate networks using either end-fire or side-fire images. Split datasets were reduced to 1000, 500, 250, and 100 2D images. For deep learning prediction, modified U-Net and U-Net++ architectures were implemented and compared using an unseen test dataset of 40 3D TRUS volumes. A 3D TRUS image quality grading scale with three factors (acquisition quality, artifact severity, and boundary visibility) was developed to assess the impact on segmentation performance. For the complete training dataset, U-Net and U-Net++ networks demonstrated equivalent performance, but when trained using split end-fire/side-fire datasets, U-Net++ significantly outperformed the U-Net. Compared to the complete training datasets, U-Net++ trained using reduced-size end-fire and side-fire datasets demonstrated equivalent performance down to 500 training images. For this dataset, image quality had no impact on segmentation performance for end-fire images but did have a significant effect for side-fire images, with boundary visibility having the largest impact. Our algorithm provided fast (<1.5 s) and accurate 3D segmentations across clinically diverse images, demonstrating generalizability and efficiency when employed on smaller datasets, supporting the potential for widespread use, even when data is scarce. The development of an image quality grading scale provides a quantitative tool for assessing segmentation performance.
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Yang, Jing, Ping Tang, Jie Chen, and Huaxiang Shen. "Application and Analysis of Imaging Characteristics of Four-Dimensional Ultrasound in the Diagnosis of Fetal Cleft Lip and Palate." Journal of Medical Imaging and Health Informatics 11, no. 1 (2021): 133–38. http://dx.doi.org/10.1166/jmihi.2021.3520.
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Objective: In this paper, four-dimensional (4D) ultrasound scanning technology and level-set-based image segmentation algorithm are used to diagnose fetal cleft lip and palate to improve the detection rate and diagnostic accuracy of fetal cleft lip and palate cleft. Methods: Fifty-six fetuses were collected, and their type-B ultrasonic examination was cleft lip and palate. Also, they were identified as cleft lip and palate deformities after delivery or induced labor. Two-dimensional (2D) and 4D ultrasound scans were performed, and an ultrasound image of the fetal face was obtained using a level set image segmentation algorithm. The characteristics of 2D and 4D ultrasound images of cleft lip and palate fetuses were compared and analyzed. After the postpartum results, the comparison and analysis of the detection rate and diagnostic accuracy of fetal cleft lip and palate with 2D and 4D ultrasound were performed. Results: (1) The level set image segmentation algorithm can obtain a clear fetal facial image; (2) Fetal cleft lip and palate 2D and 4D have different imaging performances, and 4D imaging characteristics are more prominent and easier to distinguish; (3) The accuracy rate of 4D ultrasound for simple cleft lip is 100%, and that of 2D is 88.2%. The accuracy rate of 4D for cleft lip with complete cleft palate is 77.8%, and that of 2D is 69.4%. The detection rate and diagnostic accuracy of fetal cleft lip and palate in 4D ultrasound are higher. Conclusion: The level set image segmentation algorithm can well achieve the segmentation of the fetal face and improve the accuracy of ultrasound diagnosis. 4D ultrasound has good application value in the diagnosis of fetal cleft lip and palate, but the accuracy rate is less than 100%. Therefore, further research is needed to improve its accuracy rate.
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Dr. M. Renukadevi, S. Suganyadevi,. "SEGMENTATION OF KIDNEY STONE REGION IN ULTRA SOUND IMAGEBY USING REGION PARTITION AND MOUNTING SEGMENTATION ALGORITHM (RPM)." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (2021): 512–18. http://dx.doi.org/10.17762/itii.v9i1.164.
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One of the safest techniques for disease diagnosis which can be used in any part of the body is ultrasound imaging. The cost when compared with MRI, PET etc are higher than using ultra sound images is the one of the major reason. Further, it is an efficient technique for initial diagnosis and it is free from any radiation exposure. This paper concentrates on segmentation of kidney from abdominal ultrasound images. There are many common ailments affecting kidney. Hence conducting study on this segmented image becomes easy with an efficient segmentation technique. In this paper Various algorithms to pull out kidney regions from abdominal ultrasound images which are discussed by many researchers are also investigated. Due to the complicated internal organs of the abdominal region, extraction of only the kidney region is very challenging and is the major drawback of ultrasound imaging. a new technique where the collected abdominal ultrasound image is cleaned, to remove unwanted noise produced due to various interferences has been processed by this paper, the kidney region is segmented after applying the filtering technique. the subjected to Region indicator contour segmentation method to extract the renal calculi which is the region of interest in this study is this extracted kidney image. with a reasonable number of dataset and applied the statistical performance test to check for the accuracy , the method is experimented .
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Carriere, Jay, Ron Sloboda, Nawaid Usmani, and Mahdi Tavakoli. "Autonomous Prostate Segmentation in 2D B-Mode Ultrasound Images." Applied Sciences 12, no. 6 (2022): 2994. http://dx.doi.org/10.3390/app12062994.
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Prostate brachytherapy is a treatment for prostate cancer; during the planning of the procedure, ultrasound images of the prostate are taken. The prostate must be segmented out in each of the ultrasound images, and to assist with the procedure, an autonomous prostate segmentation algorithm is proposed. The prostate contouring system presented here is based on a novel superpixel algorithm, whereby pixels in the ultrasound image are grouped into superpixel regions that are optimized based on statistical similarity measures, so that the various structures within the ultrasound image can be differentiated. An active shape prostate contour model is developed and then used to delineate the prostate within the image based on the superpixel regions. Before segmentation, this contour model was fit to a series of point-based clinician-segmented prostate contours exported from conventional prostate brachytherapy planning software to develop a statistical model of the shape of the prostate. The algorithm was evaluated on nine sets of in vivo prostate ultrasound images and compared with manually segmented contours from a clinician, where the algorithm had an average volume difference of 4.49 mL or 10.89%.
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Zhang, Yingtao, Min Xian, Heng-Da Cheng, et al. "BUSIS: A Benchmark for Breast Ultrasound Image Segmentation." Healthcare 10, no. 4 (2022): 729. http://dx.doi.org/10.3390/healthcare10040729.
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Breast ultrasound (BUS) image segmentation is challenging and critical for BUS computer-aided diagnosis (CAD) systems. Many BUS segmentation approaches have been studied in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which results in a discrepancy in performance comparison. Therefore, there is a pressing need for building a benchmark to compare existing methods using a public dataset objectively, to determine the performance of the best breast tumor segmentation algorithm available today, and to investigate what segmentation strategies are valuable in clinical practice and theoretical study. In this work, a benchmark for B-mode breast ultrasound image segmentation is presented. In the benchmark, (1) we collected 562 breast ultrasound images and proposed standardized procedures to obtain accurate annotations using four radiologists; (2) we extensively compared the performance of 16 state-of-the-art segmentation methods and demonstrated that most deep learning-based approaches achieved high dice similarity coefficient values (DSC ≥ 0.90) and outperformed conventional approaches; (3) we proposed the losses-based approach to evaluate the sensitivity of semi-automatic segmentation to user interactions; and (4) the successful segmentation strategies and possible future improvements were discussed in details.
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Khan, Muhammad Salim, Laiba Saqib, Zahir Shah, Haider Ali, and Ahmad Alshehri. "Efficient Echocardiographic Image Segmentation." Mathematical Problems in Engineering 2022 (September 10, 2022): 1–5. http://dx.doi.org/10.1155/2022/1754291.
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In this paper, we propose an improved region-based active contour method based on the development of a novel signed pressure force (SPF) function. To obtain the required boundary, the method is applied to the echocardiographic images. Ultrasound image segmentation is particularly challenging due to speckle noise, low contrast, and intensity inhomogeneity. Because of these factors, segmenting echocardiographic images is a difficult task. All of these issues are addressed by the proposed model, which detects the true boundary without any noise. The proposed model is more robust, effective, and accurate when applied to images with weak edges and inhomogeneous intensity.
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Muhammad, Muhammad, Diyar Zeebaree, Adnan Mohsin Abdulazeez Brifcani, Jwan Saeed, and Dilovan Asaad Zebari. "A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images." Journal of Applied Science and Technology Trends 1, no. 3 (2020): 78–91. http://dx.doi.org/10.38094/2020jastt1328.
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The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.
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Muhammad, Muhammad, Diyar Zeebaree, Adnan Mohsin Abdulazeez Brifcani, Jwan Saeed, and Dilovan Asaad Zebari. "Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images: A Review." Journal of Applied Science and Technology Trends 1, no. 3 (2020): 78–91. http://dx.doi.org/10.38094/jastt20201328.
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The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.
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Muhammad, Muhammad, Diyar Zeebaree, Adnan Mohsin Abdulazeez Brifcani, Jwan Saeed, and Dilovan Asaad Zebari. "A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images." Journal of Applied Science and Technology Trends 1, no. 3 (2020): 78–91. http://dx.doi.org/10.38094/jastt1328.
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The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.
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Shen, Jiaqi, Fangfang Huang, and Myers Ulrich. "Evaluation and Analysis of Cardiovascular Function in Intensive Care Unit Patients by Ultrasound Image Segmentation Based on Deep Learning." Journal of Medical Imaging and Health Informatics 10, no. 8 (2020): 1892–98. http://dx.doi.org/10.1166/jmihi.2020.3119.
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Many studies have shown that cardiovascular disease has become one of the major diseases leading to death in the world. Therefore, it is a very meaningful topic to use image segmentation technology to segment blood vessels for clinical application. In order to automatically extract the features of blood vessel images in the process of segmentation, the deep learning algorithm is combined with image segmentation technology to segment the nerve cell membrane and carotid artery images of ICU patients, and to segment the blood vessel images from a multi-dimensional perspective. The relevant data are collected to observe the effect of this model. The results show that the three-dimensional multi-scale linear filter has a good effect on carotid artery segmentation in the image segmentation of nerve cell membranes and carotid artery. When analyzing the accuracy of vascular image segmentation from network parameters and training parameters, it is found that the accuracy of the threedimensional multi-scale linear filter can reach about 85%. Therefore, it can be found that the combination of deep learning algorithm and image segmentation technology has a good segmentation effect, and the segmentation accuracy is also high. The experiment achieves the desired effect, which provides experimental basis for the clinical application of the vascular image segmentation technology.
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S, Sheela, and Sumathi M. "Enhancer for ovarian cyst segmentation using adaptive thresholding technique." Indian Journal of Science and Technology 13, no. 39 (2020): 4142–50. https://doi.org/10.17485/IJST/v13i39.1602.
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Abstract <strong>Objective:</strong> To achieve the accurate segmentation of ovarian cyst from the ultrasound images. <strong>Method:</strong> Ovarian cyst ultrasound images are taken from ultrasound images.com and sonoworld.com. The cysts are segmented using adaptive thresholding technique. The segmented image (binary image) is divided into sub blocks and then number of binary transition in each block is calculated. Based on the number of transition, the pixel values are replaced by 0 or the same pixel value is maintained. In order to measure the performance of the proposed enhancer various measures like Accuracy (ACC), Dice Coefficient (DC), Jaccard Similarity Index (JSI), Matthews correlation coefficient (MCC), Sensitivity, Specificity and Precision are measured. <strong>Findings:</strong> In order to analyse the performance of the enhancer with adaptive thresholding technique, 100 ultrasound ovarian cyst images are taken. The enhancer produced better result than the existing adaptive thresholding technique. <strong>Novelty/Application:</strong> The proposed enhancer enriches the quality of the ovarian cyst segmentation. <strong>Keywords:</strong> Segmentation; adaptive thresholding technique; ultrasound images; poly cystic ovarian syndrome; follicle; ovarian cyst
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Chandra De, Utpal, Madhabananda Das, Debashis Mishra, and Debashis Mishra. "Threshold based brain tumor image segmentation." International Journal of Engineering & Technology 7, no. 3 (2018): 1801. http://dx.doi.org/10.14419/ijet.v7i3.12425.
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Image processing is most vital area of research and application in field of medical-imaging. Especially it is a major component in medical science. Starting from radiology to ultrasound (sonography), MRI, etc. in lots of area image is the only source of diagnosis process. Now-a-days, different types of devices are being introduced to capture the internal body parts in medical science to carry the diagnosis process correctly. However, due to various reasons, the captured images need to be tuned digitally to gain the more information. These processes involve noise reduction, segmentations, thresholding etc. . Image segmentation is a process to segment the target area of image to identify the area more prominently. There are different process are evolved to perform the segmentation process, one of which is Image thresholding. Moreover there are different tools are also introduce to perform this step of image thresholding. The recent introduced tool PSO is being used here to segment the MRI scans to identify the brain lesions using image thresholding technique.
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Zifan, Ali, Katelyn Zhao, Madilyn Lee, et al. "Adaptive Evolutionary Optimization of Deep Learning Architectures for Focused Liver Ultrasound Image Segmentation." Diagnostics 15, no. 2 (2025): 117. https://doi.org/10.3390/diagnostics15020117.
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Background: Liver ultrasound segmentation is challenging due to low image quality and variability. While deep learning (DL) models have been widely applied for medical segmentation, generic pre-configured models may not meet the specific requirements for targeted areas in liver ultrasound. Quantitative ultrasound (QUS) is emerging as a promising tool for liver fat measurement; however, accurately segmenting regions of interest within liver ultrasound images remains a challenge. Methods: We introduce a generalizable framework using an adaptive evolutionary genetic algorithm to optimize deep learning models, specifically U-Net, for focused liver segmentation. The algorithm simultaneously adjusts the depth (number of layers) and width (neurons per layer) of the network, dropout, and skip connections. Various architecture configurations are evaluated based on segmentation performance to find the optimal model for liver ultrasound images. Results: The model with a depth of 4 and filter sizes of [16, 64, 128, 256] achieved the highest mean adjusted Dice score of 0.921, outperforming the other configurations, using three-fold cross-validation with early stoppage. Conclusions: Adaptive evolutionary optimization enhances the deep learning architecture for liver ultrasound segmentation. Future work may extend this optimization to other imaging modalities and deep learning architectures.
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Harkey, Matthew S., Nicholas Michel, Christopher Kuenze, et al. "Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images." CARTILAGE 13, no. 2 (2022): 194760352210930. http://dx.doi.org/10.1177/19476035221093069.
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Objective To validate a semi-automated technique to segment ultrasound-assessed femoral cartilage without compromising segmentation accuracy to a traditional manual segmentation technique in participants with an anterior cruciate ligament injury (ACL). Design We recruited 27 participants with a primary unilateral ACL injury at a pre-operative clinic visit. One investigator performed a transverse suprapatellar ultrasound scan with the participant’s ACL injured knee in maximum flexion. Three femoral cartilage ultrasound images were recorded. A single expert reader manually segmented the femoral cartilage cross-sectional area in each image. In addition, we created a semi-automatic program to segment the cartilage using a random walker-based method. We quantified the average cartilage thickness and echo-intensity for the manual and semi-automated segmentations. Intraclass correlation coefficients (ICC2,k) and Bland-Altman plots were used to validate the semi-automated technique to the manual segmentation for assessing average cartilage thickness and echo-intensity. A dice correlation coefficient was used to quantify the overlap between the segmentations created with the semi-automated and manual techniques. Results For average cartilage thickness, there was excellent reliability (ICC2,k = 0.99) and a small mean difference (+0.8%) between the manual and semi-automated segmentations. For average echo-intensity, there was excellent reliability (ICC2,k = 0.97) and a small mean difference (−2.5%) between the manual and semi-automated segmentations. The average dice correlation coefficient between the manual segmentation and semi-automated segmentation was 0.90, indicating high overlap between techniques. Conclusions Our novel semi-automated segmentation technique is a valid method that requires less technical expertise and time than manual segmentation in patients after ACL injury.
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Jeba Shiney, O., J. Amar Pratap Singh, and Priestly Shan B. "EXTRACTION OF FETAL FEATURES FROM B MODE ULTRASONOGRAMS FOR EFFICIENT DIAGNOSIS OF DOWN SYNDROME IN FIRST AND SECOND TRIMESTER." Biomedical & Pharmacology Journal 12, no. 3 (2019): 1135–39. http://dx.doi.org/10.13005/bpj/1741.
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Segmentation of ultrasound images has been found to be a tedious task due to the presence of speckle and other artifacts. The random nature of the multiplicative speckle noise and lack of demarcation of information in ultrasound images makes the segmentation a highly complex one. In this paper a modified watershed based method has been proposed for segmentation of features from Ultrasound images towards efficient diagnosis of Down Syndrome in first and second trimester. The pixels are grouped based on the pixel differences and the co- occurrence matrix is formed based on the energy and contrast. If global scheme is adopted for segmentation the high frequency edges may appear as artifacts. Hence to overcome this the wavelet transform of co-occurrence matrix is obtained and each decomposed band is subjected to an averaging filter. The process bands are thresholded using Otsu’s method and a binary image is obtained. The isolated pixels are removed by using suitable morphological operations. Then inverse wavelet transform is performed to obtain the image skeleton. The resultant image is subjected to watershed segmentation using gradients. Using the above mentioned approach we can see that the regions of interest are clearly segmented and is producing reproducible results.
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Noble, J. A. "Ultrasound image segmentation and tissue characterization." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 224, no. 2 (2009): 307–16. http://dx.doi.org/10.1243/09544119jeim604.
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Krivanek, A., and M. Sonka. "Ovarian ultrasound image analysis: follicle segmentation." IEEE Transactions on Medical Imaging 17, no. 6 (1998): 935–44. http://dx.doi.org/10.1109/42.746626.
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Archip, Neculai, Robert Rohling, Peter Cooperberg, and Hamid Tahmasebpour. "Ultrasound image segmentation using spectral clustering." Ultrasound in Medicine & Biology 31, no. 11 (2005): 1485–97. http://dx.doi.org/10.1016/j.ultrasmedbio.2005.07.005.
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Huang, Qinghua, Yaozhong Luo, and Qiangzhi Zhang. "Breast ultrasound image segmentation: a survey." International Journal of Computer Assisted Radiology and Surgery 12, no. 3 (2017): 493–507. http://dx.doi.org/10.1007/s11548-016-1513-1.
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Maolood, Ismail Yaqub, Yahya Eneid Abdulridha Al-Salhi, and Songfeng Lu. "Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm." Open Medicine 13, no. 1 (2018): 374–83. http://dx.doi.org/10.1515/med-2018-0056.
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AbstractIn this study, an effective means for detecting cancer region through different types of medical image segmentation are presented and explained. We proposed a new method for cancer segmentation on the basis of fuzzy entropy with a level set (FELs) thresholding. The proposed method was successfully utilized to segment cancer images and then efficiently performed the segmentation of test ultrasound image, brain MRI, and dermoscopy image compared with algorithms proposed in previous studies. Results showed an excellent performance of the proposed method in detecting cancer image segmentation in terms of accuracy, precision, specificity, and sensitivity measures.
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Khaledyan, Donya, Thomas J. Marini, Timothy M. Baran, Avice O’Connell, and Kevin Parker. "Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet." PLOS ONE 18, no. 12 (2023): e0289195. http://dx.doi.org/10.1371/journal.pone.0289195.
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Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it popular among researchers. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the Dice coefficient, specificity, sensitivity, and F1 score values obtained were 0.93, 0.99, 0.94, and 0.94, respectively. In addition, McNemar’s statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperformed all other models, resulting in improved breast lesion segmentation.
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Holland, Lawrence, Sofia I. Hernandez Torres, and Eric J. Snider. "Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images." Bioengineering 11, no. 2 (2024): 128. http://dx.doi.org/10.3390/bioengineering11020128.
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Medical imaging can be a critical tool for triaging casualties in trauma situations. In remote or military medicine scenarios, triage is essential for identifying how to use limited resources or prioritize evacuation for the most serious cases. Ultrasound imaging, while portable and often available near the point of injury, can only be used for triage if images are properly acquired, interpreted, and objectively triage scored. Here, we detail how AI segmentation models can be used for improving image interpretation and objective triage evaluation for a medical application focused on foreign bodies embedded in tissues at variable distances from critical neurovascular features. Ultrasound images previously collected in a tissue phantom with or without neurovascular features were labeled with ground truth masks. These image sets were used to train two different segmentation AI frameworks: YOLOv7 and U-Net segmentation models. Overall, both approaches were successful in identifying shrapnel in the image set, with U-Net outperforming YOLOv7 for single-class segmentation. Both segmentation models were also evaluated with a more complex image set containing shrapnel, artery, vein, and nerve features. YOLOv7 obtained higher precision scores across multiple classes whereas U-Net achieved higher recall scores. Using each AI model, a triage distance metric was adapted to measure the proximity of shrapnel to the nearest neurovascular feature, with U-Net more closely mirroring the triage distances measured from ground truth labels. Overall, the segmentation AI models were successful in detecting shrapnel in ultrasound images and could allow for improved injury triage in emergency medicine scenarios.
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Sheela, S. "Enhancer for ovarian cyst segmentation using adaptive thresholding technique." Indian Journal of Science and Technology 13, no. 39 (2020): 4142–50. http://dx.doi.org/10.17485/ijst/v13i39.1602.
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Objective: To achieve the accurate segmentation of ovarian cyst from the ultrasound images. Method: Ovarian cyst ultrasound images are taken from ultrasound images.com and sonoworld.com. The cysts are segmented using adaptive thresholding technique. The segmented image (binary image) is divided into sub blocks and then number of binary transition in each block is calculated. Based on the number of transition, the pixel values are replaced by 0 or the same pixel value is maintained. In order to measure the performance of the proposed enhancer various measures like Accuracy (ACC), Dice Coefficient (DC), Jaccard Similarity Index (JSI), Matthews correlation coefficient (MCC), Sensitivity, Specificity and Precision are measured. Findings: In order to analyse the performance of the enhancer with adaptive thresholding technique, 100 ultrasound ovarian cyst images are taken. The enhancer produced better result than the existing adaptive thresholding technique. Novelty/Application: The proposed enhancer enriches the quality of the ovarian cyst segmentation.
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Bargsten, Lennart, Katharina A. Riedl, Tobias Wissel, et al. "Deep learning for calcium segmentation in intravascular ultrasound images." Current Directions in Biomedical Engineering 7, no. 1 (2021): 96–100. http://dx.doi.org/10.1515/cdbme-2021-1021.
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Abstract Knowing the shape of vascular calcifications is crucial for appropriate planning and conductance of percutaneous coronary interventions. The clinical workflow can therefore benefit from automatic segmentation of calcified plaques in intravascular ultrasound (IVUS) images. To solve segmentation problems with convolutional neural networks (CNNs), large datasets are usually required. However, datasets are often rather small in the medical domain. Hence, developing and investigating methods for increasing CNN performance on small datasets can help on the way towards clinically relevant results. We compared two state-of-the-art CNN architectures for segmentation, U-Net and DeepLabV3, and investigated how incorporating auxiliary image data with vessel wall and lumen annotations improves the calcium segmentation performance by using these either for pretraining or multi-task training. DeepLabV3 outperforms U-Net with up to 6.3 % by means of the Dice coefficient and 36.5 % by means of the average Hausdorff distance. Using auxiliary data improves the segmentation performance in both cases, whereas the multi-task approach outperforms the pre-training approach. The improvements of the multi-task approach in contrast to not using auxiliary data at all is 5.7 % for the Dice coefficient and 42.9 % for the average Hausdorff distance. Automatic segmentation of calcified plaques in IVUS images is a demanding task due to their relatively small size compared to the image dimensions and due to visual ambiguities with other image structures. We showed that this problem can generally be tackled by CNNs. Furthermore, we were able to improve the performance by a multi-task learning approach with auxiliary segmentation data.
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Pregitha R., Eveline, Vinod Kumar R. S., and Ebbie Selvakumar C. "FOE NET: Segmentation of Fetal in Ultrasound Images Using V-NET." International journal of electrical and computer engineering systems 14, no. 10 (2023): 1141–49. http://dx.doi.org/10.32985/ijeces.14.10.7.
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Ultrasound is a non-invasive method to diagnose and treat medical conditions. It is becoming increasingly popular to use portable ultrasound scanning devices to reduce patient wait times and make healthcare more convenient for patients. By using ultrasound imaging, you will be able to obtain images with better quality and also gain information about soft tissues. The interference caused by tissues reflected in ultrasound waves resulted in intensified speckle sound, complicating imaging. In this paper, a novel Foe-Net has been proposed for segmenting the fetal in ultrasound images. Initially, the input US images are noise removal phase using two different filters Adaptive Gaussian Filter (AGF) and Adaptive Bilateral Filter (ABF) used to reduce the noise artifacts. Then, the US images are enhanced using CLAHE and MSR for smoothing to enhance the image quality. Finally, the denoised images are input to the V-net is used to segment the fetal in the US images. The experimental outcomes of the proposed Multi-Scale Retinex (MSR) is an image enhancement technique that improves image quality by adjusting its illumination and enhancing details. Foe-Net was measured by specific parameters such as specificity, precision, and accuracy. The proposed Foe-Net achieves an overall accuracy of 99.48%, specificity of 98.56 %, and precision of 96.82 % for segmented fetal in ultrasound images. The proposed Foe-Net attains better pre-processing outcomes at low error rates and, high SNR, high PSNR, and high SSIM values.
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Bargsten, Lennart, Silas Raschka, and Alexander Schlaefer. "Capsule networks for segmentation of small intravascular ultrasound image datasets." International Journal of Computer Assisted Radiology and Surgery 16, no. 8 (2021): 1243–54. http://dx.doi.org/10.1007/s11548-021-02417-x.
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Abstract Purpose Intravascular ultrasound (IVUS) imaging is crucial for planning and performing percutaneous coronary interventions. Automatic segmentation of lumen and vessel wall in IVUS images can thus help streamlining the clinical workflow. State-of-the-art results in image segmentation are achieved with data-driven methods like convolutional neural networks (CNNs). These need large amounts of training data to perform sufficiently well but medical image datasets are often rather small. A possibility to overcome this problem is exploiting alternative network architectures like capsule networks. Methods We systematically investigated different capsule network architecture variants and optimized the performance on IVUS image segmentation. We then compared our capsule network with corresponding CNNs under varying amounts of training images and network parameters. Results Contrary to previous works, our capsule network performs best when doubling the number of capsule types after each downsampling stage, analogous to typical increase rates of feature maps in CNNs. Maximum improvements compared to the baseline CNNs are 20.6% in terms of the Dice coefficient and 87.2% in terms of the average Hausdorff distance. Conclusion Capsule networks are promising candidates when it comes to segmentation of small IVUS image datasets. We therefore assume that this also holds for ultrasound images in general. A reasonable next step would be the investigation of capsule networks for few- or even single-shot learning tasks.
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Wang, Xinyu, Zhengqi Chang, Qingfang Zhang, Cheng Li, Fei Miao, and Gang Gao. "Prostate Ultrasound Image Segmentation Based on DSU-Net." Biomedicines 11, no. 3 (2023): 646. http://dx.doi.org/10.3390/biomedicines11030646.
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In recent years, the incidence of prostate cancer in the male population has been increasing year by year. Transrectal ultrasound (TRUS) is an important means of prostate cancer diagnosis. The accurate segmentation of the prostate in TRUS images can assist doctors in needle biopsy and surgery and is also the basis for the accurate identification of prostate cancer. Due to the asymmetric shape and blurred boundary line of the prostate in TRUS images, it is difficult to obtain accurate segmentation results with existing segmentation methods. Therefore, a prostate segmentation method called DSU-Net is proposed in this paper. This proposed method replaces the basic convolution in the U-Net model with the improved convolution combining shear transformation and deformable convolution, making the network more sensitive to border features and more suitable for prostate segmentation tasks. Experiments show that DSU-Net has higher accuracy than other existing traditional segmentation methods.
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Chang, Chenkai, Fei Qi, Chang Xu, Yiwei Shen, and Qingwu Li. "A dual-modal dynamic contour-based method for cervical vascular ultrasound image instance segmentation." Mathematical Biosciences and Engineering 21, no. 1 (2023): 1038–57. http://dx.doi.org/10.3934/mbe.2024043.
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<abstract><p><italic>Objectives:</italic> We intend to develop a dual-modal dynamic contour-based instance segmentation method that is based on carotid artery and jugular vein ultrasound and its optical flow image, then we evaluate its performance in comparison with the classic single-modal deep learning networks. <italic>Method:</italic> We collected 2432 carotid artery and jugular vein ultrasound images and divided them into training, validation and test dataset by the ratio of 8:1:1. We then used these ultrasound images to generate optical flow images with clearly defined contours. We also proposed a dual-stream information fusion module to fuse complementary features between different levels extracted from ultrasound and optical flow images. In addition, we proposed a learnable contour initialization method that eliminated the need for manual design of the initial contour, facilitating the rapid regression of nodes on the contour to the ground truth points. <italic>Results:</italic> We verified our method by using a self-built dataset of carotid artery and jugular vein ultrasound images. The quantitative metrics demonstrated a bounding box detection mean average precision of 0.814 and a mask segmentation mean average precision of 0.842. Qualitative analysis of our results showed that our method achieved smoother segmentation boundaries for blood vessels. <italic>Conclusions:</italic> The dual-modal network we proposed effectively utilizes the complementary features of ultrasound and optical flow images. Compared to traditional single-modal instance segmentation methods, our approach more accurately segments the carotid artery and jugular vein in ultrasound images, demonstrating its potential for reliable and precise medical image analysis.</p></abstract>
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Darvish, Arman, and Shahryar Rahnamayan. "Optimal Parameter Setting of Active-Contours Using Differential Evolution and Expert-Segmented Sample Image." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 6 (2012): 677–86. http://dx.doi.org/10.20965/jaciii.2012.p0677.
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Generally, tissue extraction (segmentation) is one of the most challenging tasks in medical image processing. Inaccurate segmentation propagates errors to the subsequent steps in the image processing chain. Thus, in any image processing chain, the role of segmentation is in fact critical because it has a significant impact on the accuracy of the final results, such as those of feature extraction. The appearance of variant noise types makes medical image segmentation a more complicated task. Thus far, many approaches for image segmentation have been proposed, including the well-known active contour (snake) model. This method minimizes the energy associated with the target’s contour, which is the sum of the internal and external energy. Although this model has strong characteristics, it suffers from sensitivity to its control parameters. Finding the optimal parameter values is not a trivial task, because the parameters are correlated and problem-dependent. To overcome this problem, this paper proposes a new approach for setting snake’s optimal parameters, which utilizes an expertsegmented gold (ground-truth) image and an optimization algorithm to determine the optimal values for snake’s seven control parameters. The proposed approach was tested on three different medical image test suites: prostate ultrasound (33 images), breast ultrasound (30 images), and lung X-Ray images (48 images). In the current approach, the DE algorithm is employed as a global optimizer. The scheme introduced in this paper is general enough to allow snake to be replaced by any other segmentation algorithm, such as the level set method. For experimental verification, 111 images were utilized. In a comparison with the prepared gold images, the overall error rate is shown to be less than 3%. We explain the proposed approach and the experiments in detail.
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Huang, Kuan, Yingtao Zhang, Heng-Da Cheng, and Ping Xing. "Trustworthy Breast Ultrasound Image Semantic Segmentation Based on Fuzzy Uncertainty Reduction." Healthcare 10, no. 12 (2022): 2480. http://dx.doi.org/10.3390/healthcare10122480.
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Medical image semantic segmentation is essential in computer-aided diagnosis systems. It can separate tissues and lesions in the image and provide valuable information to radiologists and doctors. The breast ultrasound (BUS) images have advantages: no radiation, low cost, portable, etc. However, there are two unfavorable characteristics: (1) the dataset size is often small due to the difficulty in obtaining the ground truths, and (2) BUS images are usually in poor quality. Trustworthy BUS image segmentation is urgent in breast cancer computer-aided diagnosis systems, especially for fully understanding the BUS images and segmenting the breast anatomy, which supports breast cancer risk assessment. The main challenge for this task is uncertainty in both pixels and channels of the BUS images. In this paper, we propose a Spatial and Channel-wise Fuzzy Uncertainty Reduction Network (SCFURNet) for BUS image semantic segmentation. The proposed architecture can reduce the uncertainty in the original segmentation frameworks. We apply the proposed method to four datasets: (1) a five-category BUS image dataset with 325 images, and (2) three BUS image datasets with only tumor category (1830 images in total). The proposed approach compares state-of-the-art methods such as U-Net with VGG-16, ResNet-50/ResNet-101, Deeplab, FCN-8s, PSPNet, U-Net with information extension, attention U-Net, and U-Net with the self-attention mechanism. It achieves 2.03%, 1.84%, and 2.88% improvements in the Jaccard index on three public BUS datasets, and 6.72% improvement in the tumor category and 4.32% improvement in the overall performance on the five-category dataset compared with that of the original U-shape network with ResNet-101 since it can handle the uncertainty effectively and efficiently.
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Wang, Yanwei, Junbo Ye, Tianxiang Wang, Jingyu Liu, Hao Dong, and Xin Qiao. "Breast Ultrasound Image Segmentation Algorithm Using Adaptive Region Growing and Variation Level Sets." Mathematical Problems in Engineering 2022 (October 3, 2022): 1–15. http://dx.doi.org/10.1155/2022/1752390.
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To address the features of strong noise, blurred boundaries, and poor imaging quality in breast ultrasound images, we propose a method for segmenting breast ultrasound images using adaptive region growing and variation level sets. First, this method builds a template layer from the difference between the marked image and the original image. Second, the Otsu algorithm is used to measure the target and background using the maximum class variance method to set the threshold. Finally, through the level set of the pixel neighborhood, the boundary points of the adaptive region growth are specified by the level set of the pixel neighborhood, and it is therefore possible to accurately determine the contour perimeter and area of the lesion region. The results demonstrate that the value of Jaccard and Dice for benign tumors is greater than 0.99. Therefore, the segmentation effect of breast images can be achieved by utilizing a breast ultrasound image segmentation approach that uses adaptive region growth and variation level sets.
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Yun, Ting, Yi Qing Xu, and Lin Cao. "Semi-Supervised Ultrasound Image Segmentation Based on Curvelet Features." Applied Mechanics and Materials 239-240 (December 2012): 104–14. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.104.
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The research is aimed at the development of an image processing system for classification of pathological area for medical images obtained from computed tomography (CT) scans. We proposed a novel semi-supervised image segmentation method based on the curvelet transform and SVM classfication. Firstly, through curvelet transform ultrasound images were decomposed into different directions and scales, the main distribution curvelet coefficients were extracted by cauchy model to reduce the algorithm time complexity, after inverse curvelet transform to obtaine a series of feature vectors from main distribution curvelet coefficients, then training samples and test samples were constructed; Secondly semi-supervised SVM classifier was designed, in order to reducing the weak classifier error rate, iteratively adjustment method was used to modify the SVM parameters, thus SVM strong classifier was constructed; Finally the expert manual tagging map were taken as reference standards, comparison with the existing method, experimental results shows that our algorithm is high anti-interference and has higher accuracy and effectiveness for ultrasound images pathological region segmentation.
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Dašić, Lazar, Nikola Radovanović, Tijana Šušteršič, Anđela Blagojević, Leo Benolić, and Nenad Filipović. "Patch-based Convolutional Neural Network for Atherosclerotic Carotid Plaque Semantic Segmentation." Ipsi Transactions on Internet research 18, no. 1 (2022): 56–61. http://dx.doi.org/10.58245/ipsi.tir.22jr.10.
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Atherosclerotic plaque deposition within the coronary vessel wall leads to arterial stenosis and if not adequately treated, it may potentially have deteriorating consequences, such as a debilitating stroke, thus making early detection of the most importance. The manual plaque components annotation process is both time and resource consuming, therefore, an automatic and accurate segmentation tool is necessary. The main aim of this paper is to present the model for identification and segmentation of the atherosclerotic plaque components such as lipid core, fibrous and calcified tissue, by using Convolutional Neural Network on patch-based segments of ultrasound images. There was some research done on the topic of plaque components segmentation, but not in ultrasound imaging data. Due to the size of some plaque components being only a couple of millimeters, we argue that training a neural network on smaller image patches will perform better than a classifier based on the whole image. Besides the size of components, this decision is motivated by the observation that plaque components are not uniformly distributed throughout the whole carotid wall and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Our model achieved good results in the segmentation of fibrous tissue but had difficulties in the segmentation of lipid and calcified tissue due to the quality of ultrasound images.
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Zhao, Yuan, Mingjie Jiang, Wai Sum Chan, and Bernard Chiu. "Development of a Three-Dimensional Carotid Ultrasound Image Segmentation Workflow for Improved Efficiency, Reproducibility and Accuracy in Measuring Vessel Wall and Plaque Volume and Thickness." Bioengineering 10, no. 10 (2023): 1217. http://dx.doi.org/10.3390/bioengineering10101217.
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Automated segmentation of carotid lumen-intima boundary (LIB) and media-adventitia boundary (MAB) by deep convolutional neural networks (CNN) from three-dimensional ultrasound (3DUS) images has made assessment and monitoring of carotid atherosclerosis more efficient than manual segmentation. However, training of CNN still requires manual segmentation of LIB and MAB. Therefore, there is a need to improve the efficiency of manual segmentation and develop strategies to improve segmentation accuracy by the CNN for serial monitoring of carotid atherosclerosis. One strategy to reduce segmentation time is to increase the interslice distance (ISD) between segmented axial slices of a 3DUS image while maintaining the segmentation reliability. We, for the first time, investigated the effect of ISD on the reproducibility of MAB and LIB segmentations. The intra-observer reproducibility of LIB and MAB segmentations at ISDs of 1 mm and 2 mm was not statistically significantly different, whereas the reproducibility at ISD = 3 mm was statistically lower. Therefore, we conclude that segmentation with an ISD of 2 mm provides sufficient reliability for CNN training. We further proposed training the CNN by the baseline images of the entire cohort of patients for automatic segmentation of the follow-up images acquired for the same cohort. We validated that segmentation with this time-based partitioning approach is more accurate than that produced by patient-based partitioning, especially at the carotid bifurcation. This study forms the basis for an efficient, reproducible, and accurate 3DUS workflow for serial monitoring of carotid atherosclerosis useful in risk stratification of cardiovascular events and in evaluating the efficacy of new treatments.
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Ardhianto, Peter, Jen-Yung Tsai, Chih-Yang Lin, et al. "A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images." Applied Sciences 11, no. 9 (2021): 4021. http://dx.doi.org/10.3390/app11094021.
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Deep learning has aided in the improvement of diagnosis identification, evaluation, and the interpretation of muscle ultrasound images, which may benefit clinical personnel. Muscle ultrasound images presents challenges such as low image quality due to noise, insufficient data, and different characteristics between skeletal and smooth muscles that can affect the effectiveness of deep learning results. From 2018 to 2020, deep learning has the improved solutions used to overcome these challenges; however, deep learning solutions for ultrasound images have not been compared to the conditions and strategies used to comprehend the current state of knowledge for handling skeletal and smooth muscle ultrasound images. This study aims to look at the challenges and trends of deep learning performance, especially in regard to overcoming muscle ultrasound image problems such as low image quality, muscle movement in skeletal muscles, and muscle thickness in smooth muscles. Skeletal muscle segmentation presents difficulties due to the regular movement of muscles and resulting noise, recording data through skipped connections, and modified layers required for upsampling. In skeletal muscle classification, the problems faced are area-specific, thus making a cropping strategy useful. Furthermore, there is no need to add additional layer modifications for smooth muscle segmentation as muscle thickness is the main problem in such cases.
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Farook, I. Mohammed, S. Dhanalakshmi, V. Manikandan, and C. Venkatesh. "Optimal Feature Selection for Carotid Artery Image Segmentation Using Evolutionary Computation." Applied Mechanics and Materials 626 (August 2014): 79–86. http://dx.doi.org/10.4028/www.scientific.net/amm.626.79.
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Atherosclerosis is hardening of arteries due to high blood pressure and high cholesterol. It causes heart attacks, stroke and peripheral vascular disease and is the major cause of death. In this paper we have attempted a method to identify the presence of plaque in carotid artery from ultrasound images. The ultrasound image is segmented using improved spatial Fuzzy c means algorithm to identify the presence of plaque in carotid artery. Spatial wavelet, Hilbert Huang Transform (HHT), Moment of Gray Level Histogram (MGLH) and Gray Level Co-occurrence Matrix (GLCM) features are extracted from ultrasound images and the feature set is reduced using genetic search process. The intima media thickness is measured using the proposed method. The IMT values are measured from the segmented image and trained using MLBPNN neural network. The neural network classifies the images into normal and abnormal.
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Dilna, Kaitheri Thacharedath, and Duraisamy Jude Hemanth. "Novel image enhancement approaches for despeckling in ultrasound images for fibroid detection in human uterus." Open Computer Science 11, no. 1 (2021): 399–410. http://dx.doi.org/10.1515/comp-2020-0140.
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Abstract Ultrasonography is an extensively used medical imaging technique for multiple reasons. It works on the basic theory of echoes from the tissues under consideration. However, the occurrence of signal dependent noise such as speckle destroys utility of ultrasound images. Speckle noise is subject to the composition of image tissue and parameters of image. It reduces the effectiveness of many image processing steps and decreases human perception of fine details form ultrasound images. In many medical image processing methods, despeckling is used as the preprocessing step before segmentation and feature extraction. Many speckle reduction filters are proposed but while combining many techniques some speckle diagnostic information should be preserved. Removal of speckle noise from ultrasound image by preserving edges and added features is a great challenging task in ultrasound image restoration. This paper aims at a comprehensive description and comparison of reduction of speckle noise of ultrasound fibroid image. Many filters are applied on ultrasound scanned images and the performance is marked in terms of some statistical measures. Even though several despeckling filters are there for speckle reduction, all are not good for ultrasound scanned images. A comparison of quality measures such as mean square error, peak signal-to-noise ratio, and signal-to-noise ratio is done in ultrasound images in despeckling.