Gotowe bibliografie tematyczne / Cancer image segmentation

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Gotowa bibliografia na temat „Cancer image segmentation”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Cancer image segmentation"

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Stevens, Michiel, Afroditi Nanou, Leon W. M. M. Terstappen, Christiane Driemel, Nikolas H. Stoecklein, and Frank A. W. Coumans. "StarDist Image Segmentation Improves Circulating Tumor Cell Detection." Cancers 14, no. 12 (2022): 2916. http://dx.doi.org/10.3390/cancers14122916.

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After a CellSearch-processed circulating tumor cell (CTC) sample is imaged, a segmentation algorithm selects nucleic acid positive (DAPI+), cytokeratin-phycoerythrin expressing (CK-PE+) events for further review by an operator. Failures in this segmentation can result in missed CTCs. The CellSearch segmentation algorithm was not designed to handle samples with high cell density, such as diagnostic leukapheresis (DLA) samples. Here, we evaluate deep-learning-based segmentation method StarDist as an alternative to the CellSearch segmentation. CellSearch image archives from 533 whole blood sample
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Wang, Duan. "Skin lesion segmentation of dermoscopy images using U-Net." Applied and Computational Engineering 6, no. 1 (2023): 7–14. http://dx.doi.org/10.54254/2755-2721/6/20230360.

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Skin cancer is one of the most threatening cancers as reported and has been on the increase over the past 10 years. The traditional methods of skin cancer segmentation are time-consuming and inefficient. U-Net is a powerful and accurate way of self-segmentation in the medical field. In order to solve this problem, this paper proposes a U-Net skin cancer segmentation system that can provide results and feedback quickly, accurately and intelligently. It is composed of two parts: Skin Image Analysis Module and Skin Image Segmentation Module. In the skin image analysis module, the system learns se
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Putri, Erlinda Ratnasari, Amar Vijai Nasrulloh, and Arfan Eko Fahrudin. "Coloring of Cervical Cancer’s Ct Images to Localize Cervical Cancer." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 2 (2015): 304. http://dx.doi.org/10.11591/ijece.v5i2.pp304-310.

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<p>Cervical cancer is the most common gynecologic cancer in women. Cervical cancer and the normal cervix usually have similar attenuations on CT images which are obtained. The normal cervix and the tumour cannot be distinguished on normal CT images. CT image of cervical cancer is used by the experts for the analysis of diseases. In this research study, CT image of cervical cancer is done with process of image segmentation and coloring. The process of image segmentation is done after the image sharpening process and the determination of cervical cancer’s area. Fuzzy C-Means is used as the
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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),
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Weishaupt, L. L., T. Vuong, A. Thibodeau-Antonacci, et al. "A121 QUANTIFYING INTER-OBSERVER VARIABILITY IN THE SEGMENTATION OF RECTAL TUMORS IN ENDOSCOPY IMAGES AND ITS EFFECTS ON DEEP LEARNING." Journal of the Canadian Association of Gastroenterology 5, Supplement_1 (2022): 140–42. http://dx.doi.org/10.1093/jcag/gwab049.120.

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Abstract Background Tumor delineation in endoscopy images is a crucial part of clinical diagnoses and treatment planning for rectal cancer patients. However, it is challenging to detect and adequately determine the size of tumors in these images, especially for inexperienced clinicians. This motivates the need for a standardized, automated segmentation method. While deep learning has proven to be a powerful tool for medical image segmentation, it requires a large quantity of high-quality annotated training data. Since the annotation of endoscopy images is prone to high inter-observer variabili
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Rasika, Joat, A. P. Thakare Dr., Ketaki Kalele Dr., and Viashali Thakare Dr. "Genetic Programming Approach for Oral Cancer Detection and its Image Restoration." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 2422–26. https://doi.org/10.31142/ijtsrd12787.

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Cancer is one of the leading causes of death in developing countries. Cancers are of different types like breast cancer, lung cancer, skin cancer and so on. Oral Cancer is one of the types of cancers. Oral cancer is a very common type of cancer. This Oral Cancer is observed in both males as well as females. It is a big challenge to detect Oral Cancer. This is a time consuming process in medical image processing. Detection and prevention of oral cancer at early stage is critical. But it increases the chances of survival. This work presents the detection of oral cancers using Image Processing. C
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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 segmentat
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Kavitha, M. S., J. Shanthini, and R. M. Bhavadharini. "ECIDS-Enhanced Cancer Image Diagnosis and Segmentation Using Artificial Neural Networks and Active Contour Modelling." Journal of Medical Imaging and Health Informatics 10, no. 2 (2020): 428–34. http://dx.doi.org/10.1166/jmihi.2020.2976.

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In the present decade, image processing techniques are extensively utilized in various medical image diagnoses, specifically in dealing with cancer images for detection and treatment in advance. The quality of the image and the accuracy are the significant factors to be considered while analyzing the images for cancer diagnosis. With that note, in this paper, an Enhanced Cancer Image Diagnosis and Segmentation (ECIDS) framework has been developed for effective detection and segmentation of lung cancer cells. Initially, the Computed Tomography lung image (CT image) has been processed for denois
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Dena Nadir George, Haitham Salman Chyad, and Raniah Ali Mustafa. "Subject Review: Diagnoses cancer diseases systems for most body's sections using image processing techniques." Global Journal of Engineering and Technology Advances 6, no. 3 (2021): 056–62. http://dx.doi.org/10.30574/gjeta.2021.6.3.0031.

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Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and
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Dena, Nadir George, Salman Chyad Haitham, and Ali Mustafa Raniah. "Subject Review: Diagnoses cancer diseases systems for most body's sections using image processing techniques." Global Journal of Engineering and Technology Advances 6, no. 3 (2021): 056–62. https://doi.org/10.5281/zenodo.4643420.

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Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and
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Rozprawy doktorskie na temat "Cancer image segmentation"

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Li, Xiaobing. "Automatic image segmentation based on level set approach: application to brain tumor segmentation in MR images." Reims, 2009. http://theses.univ-reims.fr/exl-doc/GED00001120.pdf.

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L'objectif de la thèse est de développer une segmentation automatique des tumeurs cérébrales à partir de volumes IRM basée sur la technique des « level sets ». Le fonctionnement «automatique» de ce système utilise le fait que le cerveau normal est symétrique et donc la localisation des régions dissymétriques permet d'estimer le contour initial de la tumeur. La première étape concerne le prétraitement qui consiste à corriger l'inhomogénéité de l'intensité du volume IRM et à recaler spatialement les volumes d'IRM d'un même patient à différents instants. Le plan hémisphérique du cerveau est reche
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von, Lavante Etienne. "Segmentation and sizing of breast cancer masses with ultrasound elasticity imaging." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:81225f61-6b83-405b-aed5-17b316ed586a.

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Uncertainty in the sizing of breast cancer masses is a major issue in breast screening programs, as there is a tendency to severely underestimate the sizing of malignant masses, especially with ultrasound imaging as part of the standard triple assessment. Due to this issue about 20% of all surgically treated women have to undergo a second resection, therefore the aim of this thesis is to address this issue by developing novel image analysis methods. Ultrasound elasticity imaging has been proven to have a better ability to differentiate soft tissues compared to standard B-mode. Thus a novel seg
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Wu, Ke. "3D segmentation and registration for minimal invasive prostate cancer therapy." Phd thesis, Université Rennes 1, 2014. http://tel.archives-ouvertes.fr/tel-00962028.

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The work of this Thesis is focused on image guided focal therapy of prostate cancer by High Intensity Focused Ultrasound (HIFU). Currently MRI is the only imaging technique that can locate the tumor in prostate. In contrast, the tumor is not visible in the ultrasound image which is used to guide the HIFU planning and therapy. The aim of the Thesis is to provide registration techniques of T2 MRI to ultrasound. Two approaches were explored: 1) Region-based registration. More particularly, we studied an ultrasound texture descriptors based on moments invariant to rotation and scaling. These descr
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Esmaeili, Pourfarhangi Kamyar. "Movie10: Computational image segmentation and tracking performed by LEVER." Diss., Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/584746.

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Bioengineering;<br>Ph.D.;<br>Metastasis is the leading cause of death among cancer patients. The metastatic cascade, during which cancer cells from the primary tumor reach a distant organ and form multiple secondary tumors, consists of a series of events starting with cancer cells invasion through the surrounding tissue of the primary tumor. Invading cells may perform proteolytic degradation of the surrounding extracellular matrix (ECM) and directed migration in order to disseminate through the tissue. Both of the mentioned processes are profoundly affected by several parameters originating fr
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Munnangi, Anirudh. "Innovative Segmentation Strategies for Melanoma Skin Cancer Detection." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1510916097483278.

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Al, Zu'bi Shadi Mahmoud. "3D multiresolution statistical approaches for accelerated medical image and volume segmentation." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5300.

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Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical model
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Sharma, Osheen. "Segmentation of cancer epithelium using nuclei morphology with Deep Neural Network." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280383.

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Bladder cancer (BCa) is the fourth most commonly diagnosed cancers in men and the eighth most common in women. It is an abnormal growth of tissues which develops in the bladder lining. Histological analysis of bladder tissue facilities diagnosis as well as it serves as an important tool for research. To bet- ter understand the molecular profile of bladder cancer and to detect predictive and prognostic features, microscopy methods, such as immunofluorescence (IF), are used to investigate the characteristics of bladder cancer tissue. For this project, a new method is proposed to segment cancer e
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Vachier, Corinne. "Extraction de caractéristiques, segmentation d'image et morphologie mathématique." Phd thesis, École Nationale Supérieure des Mines de Paris, 1995. http://pastel.archives-ouvertes.fr/pastel-00004230.

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Cette thèse se propose d'explorer de nouvelles méthodes morphologiques permettant d'extraire les caractéristiques des régions qui composent une image. Ces méthodes sont en- suite destinées à être appliquées au problème de la segmentation d'image. Nous présentons tout d'abord deux approches classiques du problème de l'extraction de caractéristiques : celles basées sur les granulométries (opérations de tamisage) et celles basées sur l' étude des extrema des images numériques, en consacrant une attention particulière à la notion de dynamique. La dynamique value les extrema d'une image selon le co
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Noyel, Guillaume. "Filtrage, réduction de dimension, classification et segmentation morphologique hyperspectrale." Phd thesis, École Nationale Supérieure des Mines de Paris, 2008. http://pastel.archives-ouvertes.fr/pastel-00004473.

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Le traitement d'images hyperspectrales est la généralisation de l'analyse des images couleurs, à trois composantes rouge, vert et bleu, aux images multivariées à plusieurs dizaines ou plusieurs centaines de composantes. Dans un sens général, les images hyperspectrales ne sont pas uniquement acquises dans le domaine des longueurs d'ondes mais correspondent à une description d'un pixel par un ensemble de valeurs : c'est à dire un vecteur. Chacune des composantes d'une image hyperspectrale constitue un canal spectral, et le vecteur associé à chaque pixel est appelé spectre. Pour valider la généra
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Girum, Kibrom Berihu. "Artificial intelligence for image-guided prostate brachytherapy procedures." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCI012.

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Les procédures de radiothérapie visent à exposer les cellules cancéreuses aux rayonnements ionisants. L'implantation permanente de sources radioactives à proximité des cellules cancéreuses est une technique classique pour guérir le cancer de la prostate à un stade précoce. Le processus implique l'acquisition d'images du patient, la délimitation des volumes cibles et des organes à risque à l'aide de l'imagerie, la planification du traitement, l’implantation de grains radioactifs guidées par l'image et l'évaluation post-implantatoire. L'analyse d'images médicales basée sur l'intelligence artific
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Książki na temat "Cancer image segmentation"

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El-Baz, Ayman S. Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies: Volume II. Springer Science+Business Media, LLC, 2011.

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El-Baz, Ayman S. Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies: Volume 1. Springer Science+Business Media, LLC, 2011.

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Nelikanti, Arjun. Colorectal Cancer MRI Image Segmentation Using Image Processing Techniques. GRIN Verlag GmbH, 2015.

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Mirmehdi, Majid, Jasjit S. Suri, Ayman S. El-Baz, and Rajendra Acharya U. Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies: Volume 1. Springer, 2016.

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Suri, Jasjit S., Ayman S. El-Baz, Rajendra Acharya U, and Andrew F. Laine. Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies: Volume II. Springer, 2016.

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Części książek na temat "Cancer image segmentation"

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Srivaramangai, R. "Rectal Cancer Magnetic Resonance Image Segmentation." In Computational Intelligence in Image and Video Processing. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003218111-8.

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Ren, Jintao, Kim Hochreuter, Mathis Ersted Rasmussen, Jesper Folsted Kallehauge, and Stine Sofia Korreman. "Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83274-1_2.

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Abstract Radiation therapy (RT) is a vital part of treatment for head and neck cancer, where accurate segmentation of gross tumor volume (GTV) is essential for effective treatment planning. This study investigates the use of pre-RT tumor regions and local gradient maps to enhance mid-RT tumor segmentation for head and neck cancer in MRI-guided adaptive radiotherapy. By leveraging pre-RT images and their segmentations as prior knowledge, we address the challenge of tumor localization in mid-RT segmentation. A gradient map of the tumor region from the pre-RT image is computed and applied to mid-
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Kamal, Uday, Abdul Muntakim Rafi, Rakibul Hoque, Jonathan Wu, and Md Kamrul Hasan. "Lung Cancer Tumor Region Segmentation Using Recurrent 3D-DenseUNet." In Thoracic Image Analysis. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62469-9_4.

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Lerousseau, Marvin, Marion Classe, Enzo Battistella, et al. "Weakly Supervised Pan-Cancer Segmentation Tool." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87237-3_24.

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Atchaya, A., J. P. Aashiha, and R. Vijayarajan. "Optimal Image Segmentation of Cancer Cell Images Using Heuristic Algorithms." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2250-7_26.

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Sanderson, Edward, and Bogdan J. Matuszewski. "FCN-Transformer Feature Fusion for Polyp Segmentation." In Medical Image Understanding and Analysis. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12053-4_65.

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AbstractColonoscopy is widely recognised as the gold standard procedure for the early detection of colorectal cancer (CRC). Segmentation is valuable for two significant clinical applications, namely lesion detection and classification, providing means to improve accuracy and robustness. The manual segmentation of polyps in colonoscopy images is time-consuming. As a result, the use of deep learning (DL) for automation of polyp segmentation has become important. However, DL-based solutions can be vulnerable to overfitting and the resulting inability to generalise to images captured by different
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Liu, Hui, Qi Zhang, and Yichen Liu. "Image Segmentation of Bladder Cancer Based on DeepLabv3+." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6320-8_62.

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Deshpande, Srijay, and Durga Parkhi. "SPADESegResNet: Harnessing Spatially-Adaptive Normalization for Breast Cancer Semantic Segmentation." In Medical Image Understanding and Analysis. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66955-2_24.

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Li, Yilong, Yaqi Wang, Le Dong, et al. "Light Annotation Fine Segmentation: Histology Image Segmentation Based on VGG Fusion with Global Normalisation CAM." In Computational Mathematics Modeling in Cancer Analysis. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-17266-3_12.

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Bakasa, Wilson, Clopas Kwenda, and Serestina Viriri. "Hybrid Deep Learning Model for Pancreatic Cancer Image Segmentation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73483-0_2.

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Streszczenia konferencji na temat "Cancer image segmentation"

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Luo, Meiyuan, Chao Zhang, and Yu Liu. "An Efficient Lightweight Network for Breast Cancer Segmentation." In 2024 2nd International Conference on Machine Vision, Image Processing & Imaging Technology (MVIPIT). IEEE, 2024. https://doi.org/10.1109/mvipit65697.2024.00035.

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Yang, Tong, Bo Liu, Peizhong Liu, and Yaping Ni. "MC-Net: An Endometrial Cancer Pathological Multy Class Image Segmentation Method." In 2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC). IEEE, 2024. https://doi.org/10.1109/icftic64248.2024.10912834.

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Rangnekar, Aneesh, Nishant Nadkarni, Jue Jiang, and Harini Veeraraghavan. "Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets." In Image Perception, Observer Performance, and Technology Assessment, edited by Jovan G. Brankov and Mark A. Anastasio. SPIE, 2025. https://doi.org/10.1117/12.3047709.

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Liu, Tengxiao, Huiya Yang, Jiayi Huang, and Yihan Zhou. "A Review of Colorectal Cancer Histopathology Image Segmentation Using Deep Learning Methods." In 2024 6th International Conference on Control and Robotics (ICCR). IEEE, 2024. https://doi.org/10.1109/iccr64365.2024.10927570.

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Jin, Zhikun, and Yanyan Huang. "Improved Deeplabv3+-based multi-class segmentation model of oral cancer using histopathology images." In 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), edited by Kelin Du and Azlan bin Mohd Zain. SPIE, 2024. http://dx.doi.org/10.1117/12.3038467.

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Kelsch, Carolina R., Jean Schmith, Rita F. T. Gomes, Vinicius C. Carrard, and Rodrigo Marques de Figueiredo. "Image Processing Methods for Oral Macules and Spots Segmentation." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sbcas.2023.229664.

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Oral cancers are the 16th most common type of cancer in the world and present a high mortality rate. This is mainly because they are frequently discovered in an advanced stage due to the lack of specialized professionals. Some clinical characteristics such as borders and symmetry can aid in cancer diagnosis, and therefore the segmentation of the lesions is important. In light of this, this work aimed to present and evaluate different analytic methods to perform automatic segmentation of oral macules and spots from 21 clinical images. From the tested methods, the one with the best result reache
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Araújo, Rafael Luz, Daniel de S. Luz, Bruno Vicente de Lima, et al. "Quantifying the effects of segmentation in image classification for melanoma recognition." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2024. https://doi.org/10.5753/eniac.2024.245228.

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Melanoma remains the leading cause of skin cancer-related deaths worldwide, emphasizing the critical need for early detection to enhance survival rates. Computational methods are pivotal in aiding its diagnosis through medical imaging, necessitating accurate lesion segmentation to facilitate effective interpretation. Our study investigates the comparative efficacy of skin lesion classification with and without segmentation, leveraging pre-trained convolutional neural networks (CNNs) and CapsNet architectures. Findings underscore CNNs’ superiority, highlighting segmentation’s beneficial impact
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Wu, Meng-Ling, Jui-Hung Chang, and Pau-Choo Chung. "Image Segmentation for Colorectal cancer histopathological images analysis." In 2022 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE). IEEE, 2022. http://dx.doi.org/10.1109/rasse54974.2022.9989848.

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Khoulqi, Ichrak, and Najlae Idrissi. "Breast cancer image segmentation and classification." In the 4th International Conference. ACM Press, 2019. http://dx.doi.org/10.1145/3368756.3369039.

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Hu, Mingzhe, Yuheng Li, and Xiaofeng Yang. "SkinSAM: adapting the segmentation anything model for skin cancer segmentation." In Image Perception, Observer Performance, and Technology Assessment, edited by Yan Chen and Claudia R. Mello-Thoms. SPIE, 2024. http://dx.doi.org/10.1117/12.3006837.

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