Relevant bibliographies by topics / Lung nodule segmentation

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Lung nodule segmentation'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Lung nodule segmentation"

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Joshua, Eali Stephen Neal. "LUNG NODULE SEMANTIC SEGMENTATION WITH BI-DIRECTION FEATURES USING U-INET." Journal of Medical pharmaceutical and allied sciences 10, no. 5 (2021): 3494–99. http://dx.doi.org/10.22270/jmpas.v10i5.1454.

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It's difficult to detect lung cancer and determine the severity of the disease without a CT scan of the lungs. The anonymity of nodules, as well as physical characteristics such as curvature and surrounding tissue, suggest that CT lung nodule segmentation has limitations. According to the study, a new, resource-efficient deep learning architecture dubbed U-INET is required. When a doctor orders a computed tomography (CT) scan for cancer diagnosis, precise and efficient lung nodule segmentation is required. Due to the nodules' hidden form, poor visual quality, and context, lung nodule segmentat
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Li, Rui, Chuda Xiao, Yongzhi Huang, Haseeb Hassan, and Bingding Huang. "Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review." Diagnostics 12, no. 2 (2022): 298. http://dx.doi.org/10.3390/diagnostics12020298.

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Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat to people’s health. Therefore, diagnosing lung nodules at an early stage is crucial to improving patient survival rates. Numerous computer-aided diagnosis (CAD) systems have been developed to detect and classify such nodules in their early stages. Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification. A number of review articles have considered various components of such syst
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Tang, Tiequn, Feng Li, Minshan Jiang, Xunpeng Xia, Rongfu Zhang, and Kailin Lin. "Improved Complementary Pulmonary Nodule Segmentation Model Based on Multi-Feature Fusion." Entropy 24, no. 12 (2022): 1755. http://dx.doi.org/10.3390/e24121755.

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Accurate segmentation of lung nodules from pulmonary computed tomography (CT) slices plays a vital role in the analysis and diagnosis of lung cancer. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in the automatic segmentation of lung nodules. However, they are still challenged by the large diversity of segmentation targets, and the small inter-class variances between the nodule and its surrounding tissues. To tackle this issue, we propose a features complementary network according to the process of clinical diagnosis, which made full use of the complementarity
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Usman, Muhammad, and Yeong-Gil Shin. "DEHA-Net: A Dual-Encoder-Based Hard Attention Network with an Adaptive ROI Mechanism for Lung Nodule Segmentation." Sensors 23, no. 4 (2023): 1989. http://dx.doi.org/10.3390/s23041989.

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Measuring pulmonary nodules accurately can help the early diagnosis of lung cancer, which can increase the survival rate among patients. Numerous techniques for lung nodule segmentation have been developed; however, most of them either rely on the 3D volumetric region of interest (VOI) input by radiologists or use the 2D fixed region of interest (ROI) for all the slices of computed tomography (CT) scan. These methods only consider the presence of nodules within the given VOI, which limits the networks’ ability to detect nodules outside the VOI and can also encompass unnecessary structures in t
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Maqsood, Muazzam, Sadaf Yasmin, Irfan Mehmood, Maryam Bukhari, and Mucheol Kim. "An Efficient DA-Net Architecture for Lung Nodule Segmentation." Mathematics 9, no. 13 (2021): 1457. http://dx.doi.org/10.3390/math9131457.

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A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional b
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Annavarapu, Chandra Sekhara Rao, Samson Anosh Babu Parisapogu, Nikhil Varma Keetha, Praveen Kumar Donta, and Gurindapalli Rajita. "A Bi-FPN-Based Encoder–Decoder Model for Lung Nodule Image Segmentation." Diagnostics 13, no. 8 (2023): 1406. http://dx.doi.org/10.3390/diagnostics13081406.

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Early detection and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. However, the anonymous shapes, visual features, and surroundings of the nodules as observed in the CT images pose a challenging and critical problem to the robust segmentation of lung nodules. This article proposes a resource-efficient model architecture: an end-to-end deep learning approach for lung nodule segmentation. It incorporates a Bi-FPN (bidirectional feature network) between an encoder and a decoder architecture. Furthermore, it uses the Mish activa
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Snezhko, E. V., S. A. Kharuzhyk, A. V. Tuzikov, and V. A. Kovalev. "SMALL NODULES LOCALIZATION ON CT IMAGES OF LUNGS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W4 (May 10, 2017): 141–44. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w4-141-2017.

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According to the World Health Organization (WHO) lung cancer remains the leading cause of death of men among all malignant tumors [1, 2]. One of the reasons of such a statistics is the fact that the lung cancer is hardly diagnosed on the yearly stages when it is almost asymptomatic. The purpose of this paper is to present a Computer-Aided Diagnosis (CAD) software developed for assistance of early detection of nodules in CT lung images including solitary pulmonary nodules (SPN) as well as multiple nodules. The efficiency of nodule localization was intended to be as high as the level of the best
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Lokhande, Narendra Lalchand, and Tushar Hrishikesh Jaware. "A Systematic Review of AI Based Software Test Case Optimization." International Research Journal of Multidisciplinary Scope 05, no. 04 (2024): 860–71. http://dx.doi.org/10.47857/irjms.2024.v05i04.01452.

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In the realm of computer-aided diagnosis systems designed for lung cancer, accurately segmenting nodules holds vital importance. This segmentation process has a vital role in examining the image attributes of lung nodules captured in computed tomography scans, ultimately aiding in separation of benign and cancerous nodules. Timely detection of these lesions stands as the most effective strategy in combating lung cancer, a disease notorious for its high malignancy rates across both genders. Despite numerous deep learning techniques proposed for nodule segmentation, it remains challenging due to
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Iqbal, Saleem, Khalid Iqbal, Fahim Arif, Arslan Shaukat, and Aasia Khanum. "Potential Lung Nodules Identification for Characterization by Variable Multistep Threshold and Shape Indices from CT Images." Computational and Mathematical Methods in Medicine 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/241647.

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Computed tomography (CT) is an important imaging modality. Physicians, surgeons, and oncologists prefer CT scan for diagnosis of lung cancer. However, some nodules are missed in CT scan. Computer aided diagnosis methods are useful for radiologists for detection of these nodules and early diagnosis of lung cancer. Early detection of malignant nodule is helpful for treatment. Computer aided diagnosis of lung cancer involves lung segmentation, potential nodules identification, features extraction from the potential nodules, and classification of the nodules. In this paper, we are presenting an au
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Qu, Ming Zhi, and Gui Rong Weng. "Lung Nodule Segmentation Using Mathematical Morphology." Applied Mechanics and Materials 58-60 (June 2011): 1378–83. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1378.

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Contemporary computed tomography (CT) technology offers the better potential of screening for the early detection of lung cancer than the traditional x-ray chest radiographs. In order to help improve radiologists’ diagnostic performance and efficiency, many researchers propose to develop computer-aided detection and diagnosis (CAD) system for the detection and characterization of lung nodules depicted on CT images and to evaluate its potentially clinical utility in assisting radiologists. Based on review of computer-aided detection and diagnosis of lung nodules using CT at home and abroad in r
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Dissertations / Theses on the topic "Lung nodule segmentation"

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CAMPOS, VANESSA DE OLIVEIRA. "MULTICRITERION SEGMENTATION FOR LUNG NODULE DETECTION IN COMPUTED TOMOGRAPHY." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16423@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Este trabalho propõe um novo algoritmo de segmentação baseado em crescimento de regiões para detecção de nódulos pulmonares em imagens de tomografia computadorizada. Para decidir, em cada iteração, se dois objetos adjacentes são fundidos em um único objeto, o algoritmo de segmentação calcula um índice de heterogeneidade baseada em múltiplos critérios. Entretanto, o algoritmo de segmentação depende de alguns parâmetros os quais foram encontrados utilizando algoritmo genético. Resultados experimentais mostraram que o método é robust
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Berglin, Lukas. "Design, Evaluation and Implementation of a Pipeline for Semi-Automatic Lung Nodule Segmentation." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-126075.

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Lung cancer is the most common type of cancer in the world and always manifests as lung nodules. Nodules are small tumors that consist of lung tissue. They are usually spherical in shape and their cores can be either solid or subsolid. Nodules are common in lungs, but not all of them are malignant. To determine if a nodule is malignant or benign, attributes like nodule size and volume growth are commonly used. The procedure to obtain these attributes is time consuming, and therefore calls for tools to simplify the process. The purpose of this thesis work was to investigate  the feasibility of
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Chen, Huaqing. "Analysis and processing of HRCT images of the lung for automatic segmentation and nodule detection." Thesis, University of Canterbury. Computer Science and Software Engineering, 2012. http://hdl.handle.net/10092/6742.

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Automatic lung segmentation and lung nodule detection through High- Resolution Computed Tomography (HRCT) image is a new and exciting research in the area of medical image processing and analysis. In this research, two new techniques for segmentation of lung regions and extraction of nodules on the HRCT image are proposed. An automatic lung segmentation system is proposed for identifying the lungs in HRCT lung images. First, lung regions are extracted from the HRCT images by grey-level thresholding. The lung background information is eliminated by linear scans originating from border pixels. F
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Rafael-Palou, Xavier. "Detection, quantification, malignancy prediction and growth forecasting of pulmonary nodules using deep learning in follow-up CT scans." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/672964.

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Nowadays, lung cancer assessment is a complex and tedious task mainly per- formed by radiological visual inspection of suspicious pulmonary nodules, using computed tomography (CT) scan images taken to patients over time. Several computational tools relying on conventional artificial intelligence and computer vision algorithms have been proposed for supporting lung cancer de- tection and classification. These solutions mostly rely on the analysis of indi- vidual lung CT images of patients and on the use of hand-crafted image de- scriptors. Unfortunately, this makes them unable to cope
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Li, Xuechen. "Machine learning-based lung nodule detection on chest x-ray radiographs." Thesis, 2016. http://hdl.handle.net/1959.13/1313753.

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Research Doctorate - Doctor of Philosophy (PhD)<br>Lung cancer is one of the most deadly diseases. Worldwide it has the highest rate of incidence and death of any cancer. Early diagnosis of lung cancer is the key to providing the best possible clinical outcomes for patients. As an initial diagnostic tool for a variety of clinical conditions, chest x-ray (CXR) radiography is the most commonly used radiological examination by far, making up at least a third of all examinations in a typical radiology department. Many diseases such as lung cancer can be diagnosed at an early stage by regular healt
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陳, 斌., and Bin Chen. "Automated Segmentation of Lung Nodules and Pulmonary Blood Vessels and Follow-up Analysis of Lung Nodules from 3D CT Images." Thesis, 2012. http://hdl.handle.net/2237/16427.

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Book chapters on the topic "Lung nodule segmentation"

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Campos, D. M., A. Simões, I. Ramos, and A. Campilho. "Feature-Based Supervised Lung Nodule Segmentation." In IFMBE Proceedings. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03005-0_7.

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Fetita, Catalin I., Françoise Prêteux, Catherine Beigelman-Aubry, and Philippe Grenier. "3D Automated Lung Nodule Segmentation in HRCT." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39899-8_77.

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Kamble, Bhawana, Satya Prakash Sahu, and Rajesh Doriya. "A Review on Lung and Nodule Segmentation Techniques." In Advances in Data and Information Sciences. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0694-9_52.

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Kumar, Subham, and Sundaresan Raman. "Lung Nodule Segmentation Using 3-Dimensional Convolutional Neural Networks." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0035-0_48.

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Zinoveva, Olga, Dmitriy Zinovev, Stephen A. Siena, Daniela S. Raicu, Jacob Furst, and Samuel G. Armato. "A Texture-Based Probabilistic Approach for Lung Nodule Segmentation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21596-4_3.

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Gautam, Nandita, Abhishek Basu, Dmitry Kaplun, and Ram Sarkar. "An Ensemble of UNet Frameworks for Lung Nodule Segmentation." In Current Problems in Applied Mathematics and Computer Science and Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34127-4_44.

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Yang, Han, Lu Shen, Mengke Zhang, and Qiuli Wang. "Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16443-9_5.

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Ziyad, Shabana R., V. Radha, and V. Thavavel. "Performance Evaluation of Lung Segmentation Techniques in Computer Aided Lung Nodule Detection System." In Futuristic Trends in Networks and Computing Technologies. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4451-4_49.

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Shamas, Shazia, S. N. Panda, and Ishu Sharma. "Review on Lung Nodule Segmentation-Based Lung Cancer Classification Using Machine Learning Approaches." In Artificial Intelligence on Medical Data. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0151-5_24.

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Suji, R. Jenkin, Sarita Singh Bhadouria, Joydip Dhar, and W. Wilfred Godfrey. "Optical Flow Based Background Subtraction Method for Lung Nodule Segmentation." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4015-8_23.

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Conference papers on the topic "Lung nodule segmentation"

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Li, Xinning, Mengyi Zhang, Wenjun Zhu, Yang Yi, and Lijing Sun. "MCUNet:Multi-Level Feature and Uncertainty-Guided Lung Nodule Segmentation." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10864668.

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Devi, R. Shyamala, Preetha I, and Martina Jose Mary.M. "Lung Nodule Segmentation using Alexnet and Recurrent Neural Network." In 2024 International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST). IEEE, 2024. https://doi.org/10.1109/icrisst59181.2024.10922002.

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Zafaranchi, Arman, Francesca Lizzi, Alessandra Retico, Camilla Scapicchio, and Maria Fantacci. "Explainability Applied to a Deep-Learning Based Algorithm for Lung Nodule Segmentation." In 1st International Conference on Explainable AI for Neural and Symbolic Methods. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013014600003886.

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Tang, Weixuan, Jun Wang, Rui Shi, and Guang Yang. "Integration of 3D Attention Mechanism and Atrous Convolution for Lung Nodule Segmentation." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10958022.

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Sui, Mingxiu, Jiacheng Hu, Tong Zhou, Zibo Liu, Likang Wen, and Junliang Du. "Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation." In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2024. http://dx.doi.org/10.1109/icbase63199.2024.10762674.

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Rikhari, Himanshu, Esha Baidya Kayal, Shuvadeep Ganguly, et al. "Lung Nodule Segmentation in CT Scans Using 3D U-Net Models with Inception and ResNet Architectures." In 2024 IEEE International Conference on Contemporary Computing and Communications (InC4). IEEE, 2024. http://dx.doi.org/10.1109/inc460750.2024.10649087.

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Zang, Zhiyang, Zhipeng Liu, and Lun Lin. "AMMU-Net: Automatic Multi-Scale Feature Selection Network Based on Mixture of Experts for Lung Nodule Segmentation." In 2024 International Conference on Virtual Reality and Visualization (ICVRV). IEEE, 2024. https://doi.org/10.1109/icvrv62410.2024.00045.

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Zhuang, Luoting, Seyed Mohammad Hossein Tabatabaei, Ashley E. Prosper, and William Hsu. "Enhancing Lung Segmentation Algorithms to Ensure Inclusion of Juxtapleural Nodules." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10981085.

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Niranjan Kumar, S., P. Malin Bruntha, S. Isaac Daniel, et al. "Lung Nodule Segmentation Using UNet." In 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2021. http://dx.doi.org/10.1109/icaccs51430.2021.9441977.

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Prashanthi, B., and S. P. Angelin Claret. "Lung nodule segmentation using resnodnet." In IV INTERNATIONAL SCIENTIFIC FORUM ON COMPUTER AND ENERGY SCIENCES (WFCES II 2022). AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0178020.

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