{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T12:40:10Z","timestamp":1776516010459,"version":"3.51.2"},"reference-count":183,"publisher":"Association for Computing Machinery (ACM)","issue":"10","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2026,7,31]]},"abstract":"<jats:p>Lung cancer is a pervasive and life-threatening disease that requires timely detection and treatment for improved patient outcomes. Recent advancements in image processing and deep learning techniques have opened new avenues for identifying cancer in medical images. We examine these studies across various dimensions, encompassing input data (such as data modality, preprocessing techniques, and synthetic data generation), model design (including architecture, modules, and loss functions), and evaluation aspects (covering data annotation requirements and segmentation performance). Our analysis considers mostly the recently proposed methods and adopts a systematic viewpoint to understand the impact of these choices on current trends, and identifies areas (i.e., research gaps), where future researchers can work. To facilitate easy reference and comparison, we have comprehensively summarized the key findings of the existing methodologies. The GitHub repository for this survey paper can be found here.<\/jats:p>\n                  <jats:p\/>","DOI":"10.1145\/3797901","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T20:45:51Z","timestamp":1773693951000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["From Pixels to Prognosis: A Comprehensive Review of Classical and Modern Approaches of Lung Nodule Segmentation for Improved Lung Cancer Diagnosis"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1539-0167","authenticated-orcid":false,"given":"Arup","family":"Sau","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur University","place":["Kolkata, India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4717-5783","authenticated-orcid":false,"given":"Nandita","family":"Gautam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur University","place":["Kolkata, India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6666-8845","authenticated-orcid":false,"given":"Abhishek","family":"Basu","sequence":"additional","affiliation":[{"name":"Computer Vision Department, Mohamed bin Zayed University of Artificial Intelligence","place":["Abu Dhabi, United Arab Emirates"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8813-4086","authenticated-orcid":false,"given":"Ram","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur University","place":["Kolkata, India"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,18]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"WHO. 2020. World Health Organisation. Retrieved March 22 2026 from https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cancer (2020)."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","unstructured":"Samuel Greenbank and David Howey. 2022. Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life. IEEE Transactions on Industrial Informatics 18 5 (2022) 2965\u20132973. DOI:10.1109\/TII.2021.3106593","DOI":"10.1109\/TII.2021.3106593"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","unstructured":"Lareib Fatima Talib Javaria Amin Muhammad Sharif and Mudassar Raza. 2024. Transformer-based semantic segmentation and CNN network for detection of histopathological lung cancer. Biomedical Signal Processing and Control 92 (2024) 106106. DOI:10.1016\/j.bspc.2024.106106","DOI":"10.1016\/j.bspc.2024.106106"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","unstructured":"Amir S. Moosavi Ali Mahboobi Farshid Arabzadeh Nasrin Ramezani Hossein S. Moosavi and Gholamreza Mehrpoor. 2024. Segmentation and classification of lungs CT-scan for detecting COVID-19 abnormalities by deep learning technique: U-Net model. Journal of Family Medicine and Primary Care 13 2 (Feb.2024) 691\u2013698. DOI:10.4103\/jfmpc.jfmpc_695_23Epub 2024 Mar 6.","DOI":"10.4103\/jfmpc.jfmpc_695_23"},{"key":"e_1_3_1_6_2","unstructured":"C. Kezi Selva Vijila and P. Tharcis. 2015. A survey on fuzzy clustering techniques for lung CT image segmentation. International Journal of Applied Engineering Research 10 5 (2015) 4274\u20134278."},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Yu Gu Jingqian Chi Jiaqi Liu Lidong Yang Baohua Zhang Dahua Yu Ying Zhao and Xiaoqi Lu. 2021. A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning. Computers in Biology and Medicine 137 (2021) 104806.","DOI":"10.1016\/j.compbiomed.2021.104806"},{"key":"e_1_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Ajay Mittal Rahul Hooda and Sanjeev Sofat. 2017. Lung field segmentation in chest radiographs: A historical review current status and expectations from deep learning. IET Image Processing 11 11 (2017) 937\u2013952.","DOI":"10.1049\/iet-ipr.2016.0526"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Guobin Zhang Shan Jiang Zhiyong Yang Li Gong Xiaodong Ma Zeyang Zhou Chao Bao and Qi Liu. 2018. Automatic nodule detection for lung cancer in CT images: A review. Computers in Biology and Medicine 103 (2018) 287\u2013300.","DOI":"10.1016\/j.compbiomed.2018.10.033"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Faridoddin Shariaty and Mojtaba Mousavi. 2019. Application of CAD systems for the automatic detection of lung nodules. Informatics in Medicine Unlocked 15 (2019) 100173.","DOI":"10.1016\/j.imu.2019.100173"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Stefanus Tao Hwa Kieu Abdullah Bade Mohd Hanafi Ahmad Hijazi and Hoshang Kolivand. 2020. A survey of deep learning for lung disease detection on medical images: State-of-the-art taxonomy issues and future directions. Journal of Imaging 6 12 (2020) 131.","DOI":"10.3390\/jimaging6120131"},{"key":"e_1_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Shubham Dodia B. Annappa and Padukudru A. Mahesh. 2022. Recent advancements in deep learning based lung cancer detection: A systematic review. Engineering Applications of Artificial Intelligence 116 (2022) 105490.","DOI":"10.1016\/j.engappai.2022.105490"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Arnaud Arindra Adiyoso Setio Alberto Traverso Thomas De Bel Moira SN Berens Cas Van Den Bogaard Piergiorgio Cerello Hao Chen Qi Dou Maria Evelina Fantacci Bram Geurts et\u00a0al. 2017. Validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Medical Image Analysis 42 1 (2017) 1\u201313.","DOI":"10.1016\/j.media.2017.06.015"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Hugo JWL Aerts Emmanuel Rios Velazquez Ralph TH Leijenaar Chintan Parmar Patrick Grossmann Sara Carvalho Johan Bussink Ren\u00e9 Monshouwer Benjamin Haibe-Kains Derek Rietveld et\u00a0al. 2014. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications 5 1 (2014) 4006.","DOI":"10.1038\/ncomms5644"},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Jennifer Y. Hwang Anthony F. Arredondo and Tessy K. Paul. 2014. National lung screening trial research team. Results of the two incidence screenings in the national lung screening trial. American Journal of Respiratory and Critical Care Medicine 189 8 (2014) 995.","DOI":"10.1164\/rccm.201401-0133RR"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369"},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Sema Candemir Stefan Jaeger Kannappan Palaniappan Jonathan P. Musco Rahul K. Singh Zhiyun Xue Alexandros Karargyris Sameer Antani George Thoma and Clement J. McDonald. 2013. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Transactions on Medical Imaging 33 2 (2013) 577\u2013590.","DOI":"10.1109\/TMI.2013.2290491"},{"key":"e_1_3_1_18_2","unstructured":"Samuel G. Armato III Geoffrey McLennan Luc Bidaut Michael F. McNitt-Gray Charles R. Meyer Anthony P. Reeves Binsheng Zhao Denise R. Aberle Claudia I. Henschke Eric A. Hoffman et\u00a0al. 2011. The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics 38 2 (2011) 915\u2013931."},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","unstructured":"Binsheng Zhao Leonard P. James Chaya S. Moskowitz Pingzhen Guo Michelle S. Ginsberg Robert A. Lefkowitz Yilin Qin Gregory J. Riely Mark G. Kris and Lawrence H. Schwartz. 2009. Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non\u2013small cell lung cancer. Radiology 252 1 (2009) 263\u2013272.","DOI":"10.1148\/radiol.2522081593"},{"key":"e_1_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Junji Shiraishi Shigehiko Katsuragawa Junpei Ikezoe Tsuneo Matsumoto Takeshi Kobayashi Ken-ichi Komatsu Mitate Matsui Hiroshi Fujita Yoshie Kodera and Kunio Doi. 2000. Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists\u2019 detection of pulmonary nodules. American Journal of Roentgenology 174 1 (2000) 71\u201374.","DOI":"10.2214\/ajr.174.1.1740071"},{"key":"e_1_3_1_21_2","unstructured":"A. Karthikeyan and M. Valliammai. 2012. Lungs segmentation using multi-level thresholding in CT images. Int. J. Electron. Comput. Sci. Eng 1 3 (2012) 1509\u20131513."},{"key":"e_1_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Brahim Ait Skourt Abdelhamid El Hassani and Aicha Majda. 2018. Lung CT image segmentation using deep neural networks. Procedia Computer Science 127 (2018) 109\u2013113.","DOI":"10.1016\/j.procs.2018.01.104"},{"key":"e_1_3_1_23_2","first-page":"275","volume-title":"Proceedings of the 3rd International Conference on Natural Computation (ICNC 2007)","volume":"2","author":"Gao Qixin","year":"2007","unstructured":"Qixin Gao, ShengJun Wang, Dazhe Zhao, and Jiren Liu. 2007. Accurate lung segmentation for X-ray CT images. In Proceedings of the 3rd International Conference on Natural Computation (ICNC 2007), Vol. 2. IEEE, 275\u2013279."},{"key":"e_1_3_1_24_2","doi-asserted-by":"crossref","unstructured":"Johnatan Carvalho Souza Jo\u00e3o Ot\u00e1vio Bandeira Diniz Jonnison Lima Ferreira Giovanni Lucca Fran\u00e7a da Silva Aristofanes Correa Silva and Anselmo Cardoso de Paiva. 2019. An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks. Computer Methods and Programs in Biomedicine 177 (2019) 285\u2013296.","DOI":"10.1016\/j.cmpb.2019.06.005"},{"key":"e_1_3_1_25_2","doi-asserted-by":"crossref","unstructured":"Zhang Li Jiehua Zhang Tao Tan Xichao Teng Xiaoliang Sun Hong Zhao Lihong Liu Yang Xiao Byungjae Lee Yilong Li et\u00a0al. 2020. Deep learning methods for lung cancer segmentation in whole-slide histopathology images\u2014the acdc@ lunghp challenge 2019. IEEE Journal of Biomedical and Health Informatics 25 2 (2020) 429\u2013440.","DOI":"10.1109\/JBHI.2020.3039741"},{"key":"e_1_3_1_26_2","doi-asserted-by":"crossref","unstructured":"Rui Xu Zhizhen Wang Zhenbing Liu Chu Han Lixu Yan Huan Lin Zeyan Xu Zhengyun Feng Changhong Liang Xin Chen et\u00a0al. 2022. Histopathological tissue segmentation of lung cancer with bilinear CNN and soft attention. BioMed Research International 2022 Article 7966553 (2022).","DOI":"10.1155\/2022\/7966553"},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","unstructured":"Qinhua Hu Luis Fabricio de F. Souza Gabriel Bandeira Holanda Shara S. A. Alves Francisco Hercules dos S. Silva Tao Han and Pedro P. Reboucas Filho. 2020. An effective approach for CT lung segmentation using mask region-based convolutional neural networks. Artificial Intelligence in Medicine 103 (2020) 101792.","DOI":"10.1016\/j.artmed.2020.101792"},{"key":"e_1_3_1_28_2","doi-asserted-by":"crossref","unstructured":"Tao Peng Caishan Wang You Zhang and Jing Wang. 2022. H-SegNet: Hybrid segmentation network for lung segmentation in chest radiographs using mask region-based convolutional neural network and adaptive closed polyline searching method. Physics in Medicine & Biology 67 7 (2022) 075006.","DOI":"10.1088\/1361-6560\/ac5d74"},{"key":"e_1_3_1_29_2","doi-asserted-by":"crossref","unstructured":"Shuangfeng Dai Ke Lu Jiyang Dong Yifei Zhang and Yong Chen. 2015. A novel approach of lung segmentation on chest CT images using graph cuts. Neurocomputing 168 (2015) 799\u2013807.","DOI":"10.1016\/j.neucom.2015.05.044"},{"key":"e_1_3_1_30_2","doi-asserted-by":"crossref","unstructured":"Ayesha Fatima Anam Tariq Mahmood Akhtar and Hira Zahid. 2019. Segmentation of chest radiographs for tuberculosis screening using kernel mapping and graph cuts. Applications of Intelligent Technologies in Healthcare (2019) 25\u201334.","DOI":"10.1007\/978-3-319-96139-2_3"},{"key":"e_1_3_1_31_2","doi-asserted-by":"crossref","unstructured":"Wei Ju Deihui Xiang Bin Zhang Lirong Wang Ivica Kopriva and Xinjian Chen. 2015. Random walk and graph cut for co-segmentation of lung tumor on PET-CT images. IEEE Transactions on Image Processing 24 12 (2015) 5854\u20135867.","DOI":"10.1109\/TIP.2015.2488902"},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","unstructured":"Mohd Hanafi Ahmad Hijazi Stefanus Kieu Tao Hwa Abdullah Bade Razali Yaakob and Mohammad Saffree Jeffree. 2019. Ensemble deep learning for tuberculosis detection using chest X-Ray and canny edge detected images. IAES International Journal of Artificial Intelligence 8 4 (2019) 429.","DOI":"10.11591\/ijai.v8.i4.pp429-435"},{"key":"e_1_3_1_33_2","doi-asserted-by":"crossref","unstructured":"Benjamin Hell Marc Kassubeck Pablo Bauszat Martin Eisemann and Marcus Magnor. 2015. An approach toward fast gradient-based image segmentation. IEEE Transactions on Image Processing 24 9 (2015) 2633\u20132645.","DOI":"10.1109\/TIP.2015.2419078"},{"key":"e_1_3_1_34_2","unstructured":"GT Shrivakshan and Chandramouli Chandrasekar. 2012. A comparison of various edge detection techniques used in image processing. International Journal of Computer Science Issues (IJCSI) 9 5 (2012) 269."},{"key":"e_1_3_1_35_2","doi-asserted-by":"crossref","unstructured":"Pune Vit. 2016. Comparison of various edge detection technique. Int. J. Signal Process. Image Process. Pattern Recognit 9 2 (2016) 143\u2013158.","DOI":"10.14257\/ijsip.2016.9.2.13"},{"key":"e_1_3_1_36_2","first-page":"532","volume-title":"Proceedings of the 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)","author":"Anand Ashish","year":"2015","unstructured":"Ashish Anand, Sanjaya Shankar Tripathy, and R. Sukesh Kumar. 2015. An improved edge detection using morphological Laplacian of Gaussian operator. In Proceedings of the 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 532\u2013536."},{"key":"e_1_3_1_37_2","doi-asserted-by":"crossref","unstructured":"Sheng Chen Liping Yao and Bao Chen. 2016. A parameterized logarithmic image processing method with Laplacian of Gaussian filtering for lung nodule enhancement in chest radiographs. Medical & Biological Engineering & Computing 54 11 (2016) 1793\u20131806.","DOI":"10.1007\/s11517-016-1469-x"},{"key":"e_1_3_1_38_2","doi-asserted-by":"crossref","unstructured":"Qi Mao Shuguang Zhao Dongbing Tong Shengchao Su Zhiwei Li and Xiang Cheng. 2021. Hessian-MRLoG: Hessian information and multi-scale reverse LoG filter for pulmonary nodule detection. Computers in Biology and Medicine 131 (2021) 104272.","DOI":"10.1016\/j.compbiomed.2021.104272"},{"key":"e_1_3_1_39_2","doi-asserted-by":"crossref","unstructured":"Soudeh Saien Abdol Hamid Pilevar and Hamid Abrishami Moghaddam. 2014. Refinement of lung nodule candidates based on local geometric shape analysis and Laplacian of Gaussian kernels. Computers in Biology and Medicine 54 (2014) 188\u2013198.","DOI":"10.1016\/j.compbiomed.2014.09.010"},{"key":"e_1_3_1_40_2","unstructured":"Sergei V. Fotin David F. Yankelevitz Claudia I. Henschke and Anthony P. Reeves. 2019. A multiscale Laplacian of Gaussian (LoG) filtering approach to pulmonary nodule detection from whole-lung CT scans. arXiv:1907.08328. Retrieved from https:\/\/arxiv.org\/abs\/1907.08328"},{"key":"e_1_3_1_41_2","unstructured":"K. Bhargavi and S. Jyothi. 2014. A survey on threshold based segmentation technique in image processing. International Journal of Innovative Research and Development 3 12 (2014) 234\u2013239."},{"key":"e_1_3_1_42_2","doi-asserted-by":"crossref","unstructured":"Azrin Khan Rachael Garner Marianna La Rocca Sana Salehi and Dominique Duncan. 2022. A novel threshold-based segmentation method for quantification of COVID-19 lung abnormalities. Signal Image and Video Processing 17 3 (2022) 907\u2013914.","DOI":"10.1007\/s11760-022-02183-6"},{"key":"e_1_3_1_43_2","first-page":"495","volume-title":"Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging","author":"Christie Jaryd R.","year":"2022","unstructured":"Jaryd R. Christie, Omar Daher, Hannah van Dongen, Rory Gilliland, Mohamed Abdelrazek, and Sarah A. Mattonen. 2022. A semi-automatic threshold-based segmentation algorithm for lung cancer delineation. In Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, Vol. 12036. SPIE, 495\u2013500."},{"key":"e_1_3_1_44_2","first-page":"2267","volume-title":"Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics","author":"Zhou Hailing","year":"2015","unstructured":"Hailing Zhou, Dmitry B. Goldgof, Samuel Hawkins, Lei Wei, Ying Liu, Doug Creighton, Robert J. Gillies, Lawrence O. Hall, and Saeid Nahavandi. 2015. A robust approach for automated lung segmentation in thoracic CT. In Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 2267\u20132272."},{"key":"e_1_3_1_45_2","doi-asserted-by":"crossref","unstructured":"Jing Gong Ji-yu Liu Li-jia Wang Bin Zheng and Sheng-dong Nie. 2016. Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier. Physica Medica 32 12 (2016) 1502\u20131509.","DOI":"10.1016\/j.ejmp.2016.11.001"},{"key":"e_1_3_1_46_2","first-page":"210","volume-title":"Proceedings of the 2018 International Conference on Inventive Research in Computing Applications (ICIRCA)","author":"Kasu Narsimha Raj","year":"2018","unstructured":"Narsimha Raj Kasu and Chandran Saravanan. 2018. Segmentation on chest radiographs using Otsu\u2019s and K-means clustering methods. In Proceedings of the 2018 International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 210\u2013213."},{"key":"e_1_3_1_47_2","doi-asserted-by":"crossref","unstructured":"Ji-kui Liu Hong-yang Jiang Meng-di Gao Chen-guang He Yu Wang Pu Wang He Ma and Ye Li. 2017. An assisted diagnosis system for detection of early pulmonary nodule in computed tomography images. Journal of Medical Systems 41 2 Article 30 (2017) 1\u20139.","DOI":"10.1007\/s10916-016-0669-0"},{"key":"e_1_3_1_48_2","doi-asserted-by":"crossref","unstructured":"Zhibin Wang Kaiyi Wang Feng Yang Shouhui Pan and Yanyun Han. 2018. Image segmentation of overlapping leaves based on Chan\u2013Vese model and Sobel operator. Information Processing in Agriculture 5 1 (2018) 1\u201310.","DOI":"10.1016\/j.inpa.2017.09.005"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3384613.3384624"},{"key":"e_1_3_1_50_2","first-page":"1","volume-title":"Proceedings of the 2016 International Conference on Inventive Computation Technologies (ICICT)","volume":"3","author":"Avinash S.","year":"2016","unstructured":"S. Avinash, K. Manjunath, and S. Senthil Kumar. 2016. An improved image processing analysis for the detection of lung cancer using Gabor filters and watershed segmentation technique. In Proceedings of the 2016 International Conference on Inventive Computation Technologies (ICICT), Vol. 3. IEEE, 1\u20136."},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","unstructured":"Tamanna Tajrin Mamun Ahmed and Sabina Zaman. 2022. Detection of lung cancer stages on computed tomography image using Laplacian filter and marker controlled watershed segmentation technique. Periodica Polytechnica Electrical Engineering and Computer Science 66 2 (2022) 105\u2013115.","DOI":"10.3311\/PPee.19755"},{"key":"e_1_3_1_52_2","first-page":"1","volume-title":"Proceedings of the 2020 International Conference for Emerging Technology (INCET)","author":"Basha CMAK Zeelan","year":"2020","unstructured":"CMAK Zeelan Basha, B. Lakshmi Pravallika, D. Vineela, and S. Lakshmi Prathyusha. 2020. An effective and robust cancer detection in the lungs with BPNN and watershed segmentation. In Proceedings of the 2020 International Conference for Emerging Technology (INCET). IEEE, 1\u20136."},{"key":"e_1_3_1_53_2","doi-asserted-by":"crossref","unstructured":"Yanyan Wu and Qian Li. 2022. The algorithm of watershed color image segmentation based on morphological gradient. Sensors 22 21 (2022) 8202.","DOI":"10.3390\/s22218202"},{"key":"e_1_3_1_54_2","doi-asserted-by":"crossref","unstructured":"Ald\u00edsio G. Medeiros Matheus T. Guimar\u00e3es Solon A. Peixoto Lucas de O. Santos Ant\u00f4nio C. da Silva Barros Eliz\u00e2ngela de S. Rebou\u00e7as Victor Hugo C. de Albuquerque and Pedro P. Rebou\u00e7as Filho. 2019. A new fast morphological geodesic active contour method for lung CT image segmentation. Measurement 148 (2019) 106687.","DOI":"10.1016\/j.measurement.2019.05.078"},{"key":"e_1_3_1_55_2","doi-asserted-by":"crossref","unstructured":"K. Sathish Y. V. Narayana Mahammad Shareef Mekala Patan Rizwan and Suresh Kallam. 2022. Efficient tumor volume measurement and segmentation approach for CT image based on twin support vector machines. Neural Computing and Applications 34 9 (2022) 7199\u20137207.","DOI":"10.1007\/s00521-021-06769-y"},{"key":"e_1_3_1_56_2","doi-asserted-by":"crossref","unstructured":"Shiwen Shen Alex A. T. Bui Jason Cong and William Hsu. 2015. An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Computers in Biology and Medicine 57 (2015) 139\u2013149.","DOI":"10.1016\/j.compbiomed.2014.12.008"},{"key":"e_1_3_1_57_2","doi-asserted-by":"crossref","unstructured":"Gregory Z. Ferl Kai H. Barck Jasmine Patil Skander Jemaa Evelyn J. Malamut Anthony Lima Jason E. Long Jason H. Cheng Melissa R. Junttila and Richard A. D. Carano. 2022. Automated segmentation of lungs and lung tumors in mouse micro-CT scans. Iscience 25 12 (2022) 105712.","DOI":"10.1016\/j.isci.2022.105712"},{"key":"e_1_3_1_58_2","doi-asserted-by":"crossref","unstructured":"Heba M. Afify Ashraf Darwish Kamel K. Mohammed and Aboul Ella Hassanien. 2020. An automated CAD system of CT chest images for COVID-19 based on genetic algorithm and K-nearest neighbor classifier. Ing\u00e9nierie des Syst\u00e8mes d Inf. 25 5 (2020) 589\u2013594.","DOI":"10.18280\/isi.250505"},{"key":"e_1_3_1_59_2","doi-asserted-by":"crossref","unstructured":"Tao Peng Thomas Canhao Xu Yihuai Wang and Fanzhang Li. 2022. Deep belief network and closed polygonal line for lung segmentation in chest radiographs. The Computer Journal 65 5 (2022) 1107\u20131128.","DOI":"10.1093\/comjnl\/bxaa148"},{"key":"e_1_3_1_60_2","doi-asserted-by":"crossref","unstructured":"Malathi Murugesan Kalaiselvi Kaliannan Shankarlal Balraj Kokila Singaram Thenmalar Kaliannan and Johny Renoald Albert. 2022. A hybrid deep learning model for effective segmentation and classification of lung nodules from CT images. Journal of Intelligent & Fuzzy Systems 42 3 (2022) 2667\u20132679.","DOI":"10.3233\/JIFS-212189"},{"key":"e_1_3_1_61_2","first-page":"170","volume-title":"Proceedings of the 2020 4th International Conference on Computing Methodologies and Communication (ICCMC)","author":"Sharma Srishti","year":"2020","unstructured":"Srishti Sharma, Prasenjeet Fulzele, and Indu Sreedevi. 2020. Hybrid model for lung nodule segmentation based on support vector machine and k-nearest neighbor. In Proceedings of the 2020 4th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 170\u2013175."},{"key":"e_1_3_1_62_2","doi-asserted-by":"crossref","unstructured":"Sotiris B. Kotsiantis. 2013. Decision trees: A recent overview. Artificial Intelligence Review 39 4 (2013) 261\u2013283.","DOI":"10.1007\/s10462-011-9272-4"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","unstructured":"Wei Guo Uday K. Rage and Seishi Ninomiya. 2013. Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model. Computers and Electronics in Agriculture 96 (2013) 58\u201366. DOI:10.1016\/j.compag.2013.04.010","DOI":"10.1016\/j.compag.2013.04.010"},{"key":"e_1_3_1_64_2","doi-asserted-by":"crossref","unstructured":"Seung Hoon Yoo Hui Geng Tin Lok Chiu Siu Ki Yu Dae Chul Cho Jin Heo Min Sung Choi Il Hyun Choi Cong Cung Van Nguen Viet Nhung et\u00a0al. 2020. Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging. Frontiers in Medicine 7 (2020) 427.","DOI":"10.3389\/fmed.2020.00427"},{"key":"e_1_3_1_65_2","doi-asserted-by":"crossref","unstructured":"Beatrice Berthon Christopher Marshall Mererid Evans and Emiliano Spezi. 2016. ATLAAS: An automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography. Physics in Medicine & Biology 61 13 (2016) 4855.","DOI":"10.1088\/0031-9155\/61\/13\/4855"},{"key":"e_1_3_1_66_2","doi-asserted-by":"crossref","unstructured":"Daniel Markel Curtis Caldwell Hamideh Alasti Hany Soliman Yee Ung Justin Lee and Alexander Sun. 2013. Automatic segmentation of lung carcinoma using 3D texture features in 18-FDG PET\/CT. International Journal of Molecular Imaging 2013 Article 980769 (2013).","DOI":"10.1155\/2013\/980769"},{"key":"e_1_3_1_67_2","doi-asserted-by":"crossref","unstructured":"Caixia Liu Ruibin Zhao and Mingyong Pang. 2019. Lung segmentation based on random forest and multi-scale edge detection. IET Image Processing 13 10 (2019) 1745\u20131754.","DOI":"10.1049\/iet-ipr.2019.0130"},{"key":"e_1_3_1_68_2","doi-asserted-by":"crossref","unstructured":"Caixia Liu Ruibin Zhao and Mingyong Pang. 2020. A fully automatic segmentation algorithm for CT lung images based on random forest. Medical Physics 47 2 (2020) 518\u2013529.","DOI":"10.1002\/mp.13939"},{"key":"e_1_3_1_69_2","doi-asserted-by":"crossref","unstructured":"May Phu Paing Kazuhiko Hamamoto Supan Tungjitkusolmun Sarinporn Visitsattapongse and Chuchart Pintavirooj. 2020. Automatic detection of pulmonary nodules using three-dimensional chain coding and optimized random forest. Applied Sciences 10 7 (2020) 2346.","DOI":"10.3390\/app10072346"},{"key":"e_1_3_1_70_2","doi-asserted-by":"crossref","unstructured":"Qiang Li Lei Chen Xiangju Li Xiaofeng Lv Shuyue Xia and Yan Kang. 2020. PRF-RW: A progressive random forest-based random walk approach for interactive semi-automated pulmonary lobes segmentation. International Journal of Machine Learning and Cybernetics 11 10 (2020) 2221\u20132235.","DOI":"10.1007\/s13042-020-01111-9"},{"key":"e_1_3_1_71_2","first-page":"352","volume-title":"Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016, Part II 19","author":"Wang Guotai","year":"2016","unstructured":"Guotai Wang, Maria A. Zuluaga, Rosalind Pratt, Michael Aertsen, Tom Doel, Maria Klusmann, Anna L. David, Jan Deprest, Tom Vercauteren, and S\u00e9bastien Ourselin. 2016. Dynamically balanced online random forests for interactive scribble-based segmentation. In Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016, Part II 19. Springer, 352\u2013360."},{"key":"e_1_3_1_72_2","doi-asserted-by":"crossref","unstructured":"Faridoddin Shariaty Mahdi Orooji Elena N. Velichko and Sergey V. Zavjalov. 2022. Texture appearance model a new model-based segmentation paradigm application on the segmentation of lung nodule in the CT scan of the chest. Computers in Biology and Medicine 140 (2022) 105086.","DOI":"10.1016\/j.compbiomed.2021.105086"},{"key":"e_1_3_1_73_2","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/978-981-15-6329-4_28","volume-title":"Advances in Biomedical Engineering and Technology: Select Proceedings of ICBEST 2018","author":"Sahu Satya Prakash","year":"2021","unstructured":"Satya Prakash Sahu, Rahul Kumar, Narendra D. Londhe, and Shrish Verma. 2021. Segmentation of lungs in thoracic CTs using K-means clustering and morphological operations. In Advances in Biomedical Engineering and Technology: Select Proceedings of ICBEST 2018. Springer, 331\u2013343."},{"key":"e_1_3_1_74_2","doi-asserted-by":"crossref","unstructured":"Michelle Hershman Bardia Yousefi Lacey Serletti Maya Galperin-Aizenberg Leonid Roshkovan Jos\u00e9 Marcio Luna Jeffrey C. Thompson Charu Aggarwal Erica L. Carpenter Despina Kontos et\u00a0al. 2021. Impact of interobserver variability in manual segmentation of non-small cell lung cancer (NSCLC) applying low-rank radiomic representation on computed tomography. Cancers 13 23 (2021) 5985.","DOI":"10.3390\/cancers13235985"},{"key":"e_1_3_1_75_2","doi-asserted-by":"publisher","unstructured":"K. Lan J. Zhou X. Jiang J. Wang S. Huang J. Yang Q. Song R. Tang X. Gong K. Liu et\u00a0al. 2023. Group theoretic particle swarm optimization for multi-level threshold lung cancer image segmentation. Quantitative Imaging in Medicine and Surgery 13 3 (32023) 1312\u20131322. DOI:10.21037\/qims-22-295Epub 2022 Oct 8.","DOI":"10.21037\/qims-22-295"},{"key":"e_1_3_1_76_2","doi-asserted-by":"publisher","unstructured":"Shabana R. Ziyad V. Radha and Thavavel Vayyapuri. 2022. A novel lung extraction approach for LDCT images using discrete wavelet transform with adaptive thresholding and Fuzzy C-means clustering enhanced by genetic algorithm. Research on Biomedical Engineering 38 2 (2022) 581\u2013598. DOI:10.1007\/s42600-022-00210-6","DOI":"10.1007\/s42600-022-00210-6"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","unstructured":"Supiksha Jain Sanjeev Indora and Dinesh Kumar Atal. 2021. Lung nodule segmentation using Salp Shuffled Shepherd Optimization Algorithm-based Generative Adversarial Network. Computers in Biology and Medicine 137 (2021) 104811. DOI:10.1016\/j.compbiomed.2021.104811","DOI":"10.1016\/j.compbiomed.2021.104811"},{"key":"e_1_3_1_78_2","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1007\/978-981-15-1925-3_36","volume-title":"Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health","author":"Wang Yixin","year":"2019","unstructured":"Yixin Wang, Jinshun Ding, Weiqing Fang, and Jian Cao. 2019. Segmentation-assisted diagnosis of pulmonary nodule recognition based on adaptive particle swarm image algorithm. In Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. Huansheng Ning (Ed.), Springer Singapore, Singapore, 504\u2013512."},{"key":"e_1_3_1_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIIECS.2015.7193181"},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","unstructured":"Juan\u2013juan Zhao Guo\u2013hua Ji Yong Xia and Xiao\u2013long Zhang. 2015. Cavitary nodule segmentation in computed tomography images based on self\u2013generating neural networks and particle swarm optimisation. International Journal of Bio-Inspired Computation 7 1 (2015) 62\u201367. DOI:10.1504\/IJBIC.2015.067999arXiv:https:\/\/www.inderscienceonline.com\/doi\/pdf\/10.1504\/IJBIC.2015.067999","DOI":"10.1504\/IJBIC.2015.067999"},{"key":"e_1_3_1_81_2","doi-asserted-by":"publisher","unstructured":"P. Badura and E. Pietka. 2014. Soft computing approach to 3D lung nodule segmentation in CT. Computers in Biology and Medicine 53 (2014) 230\u2013243. DOI:10.1016\/j.compbiomed.2014.08.005","DOI":"10.1016\/j.compbiomed.2014.08.005"},{"key":"e_1_3_1_82_2","first-page":"505","volume-title":"Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","author":"Zhao Tianyi","year":"2018","unstructured":"Tianyi Zhao, Dashan Gao, Jiao Wang, and Zhaozheng Yin. 2018. Lung segmentation in CT images using a fully convolutional neural network with multi-instance and conditional adversary loss. In Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 505\u2013509."},{"key":"e_1_3_1_83_2","doi-asserted-by":"crossref","unstructured":"Ying Chen Yerong Wang Fei Hu and Ding Wang. 2020. A lung dense deep convolution neural network for robust lung parenchyma segmentation. IEEE Access 8 (2020) 93527\u201393547.","DOI":"10.1109\/ACCESS.2020.2993953"},{"key":"e_1_3_1_84_2","doi-asserted-by":"crossref","unstructured":"Haichao Cao Hong Liu Enmin Song Chih-Cheng Hung Guangzhi Ma Xiangyang Xu Renchao Jin and Jianguo Lu. 2020. Dual-branch residual network for lung nodule segmentation. Applied Soft Computing 86 (2020) 105934.","DOI":"10.1016\/j.asoc.2019.105934"},{"key":"e_1_3_1_85_2","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/978-3-319-67558-9_11","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 3rd International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, September 14, Proceedings 3","author":"Hwang Sangheum","year":"2017","unstructured":"Sangheum Hwang and Sunggyun Park. 2017. Accurate lung segmentation via network-wise training of convolutional networks. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 3rd International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, September 14, Proceedings 3. Springer, 92\u201399."},{"key":"e_1_3_1_86_2","doi-asserted-by":"crossref","unstructured":"Xia Huang Wenqing Sun Tzu-Liang Bill Tseng Chunqiang Li and Wei Qian. 2019. Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks. Computerized Medical Imaging and Graphics 74 (2019) 25\u201336.","DOI":"10.1016\/j.compmedimag.2019.02.003"},{"key":"e_1_3_1_87_2","doi-asserted-by":"crossref","unstructured":"Zhaohui Bu Xuejun Zhang Jianxiang Lu Huan Lao Chan Liang Xianfu Xu Yini Wei and Hongjie Zeng. 2022. Lung nodule detection based on YOLOv3 deep learning with limited datasets. Molecular & Cellular Biomechanics 19 1 (2022) 17\u201328.","DOI":"10.32604\/mcb.2022.018318"},{"key":"e_1_3_1_88_2","first-page":"266","volume-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Proceedings, Part VI 22","author":"Tang Hao","year":"2019","unstructured":"Hao Tang, Chupeng Zhang, and Xiaohui Xie. 2019. Nodulenet: Decoupled false positive reduction for pulmonary nodule detection and segmentation. In Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Proceedings, Part VI 22. Springer, 266\u2013274."},{"key":"e_1_3_1_89_2","doi-asserted-by":"crossref","unstructured":"Shuo Wang Mu Zhou Zaiyi Liu Zhenyu Liu Dongsheng Gu Yali Zang Di Dong Olivier Gevaert and Jie Tian. 2017. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Medical Image Analysis 40 (2017) 172\u2013183.","DOI":"10.1016\/j.media.2017.06.014"},{"key":"e_1_3_1_90_2","first-page":"1752","volume-title":"Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":"Wang Shuo","year":"2017","unstructured":"Shuo Wang, Mu Zhou, Olivier Gevaert, Zhenchao Tang, Di Dong, Zhenyu Liu, and Tian Jie. 2017. A multi-view deep convolutional neural networks for lung nodule segmentation. In Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 1752\u20131755."},{"key":"e_1_3_1_91_2","doi-asserted-by":"crossref","unstructured":"Sihui Wang Ailian Jiang Xiaotian Li Yanfang Qiu Mengyang Li and Feixiang Li. 2022. DPBET: A dual-path lung nodules segmentation model based on boundary enhancement and hybrid transformer. Computers in Biology and Medicine 151 (2022) 106330.","DOI":"10.1016\/j.compbiomed.2022.106330"},{"key":"e_1_3_1_92_2","first-page":"175","volume-title":"Proceedings of the 2022 IEEE International Conference on Consumer Electronics-Taiwan","author":"Chang Chuan-Yu","year":"2022","unstructured":"Chuan-Yu Chang, Tin-Kwang Lin, Chih-Wen Lin, and Hsin-Tien Cheng. 2022. Application of TransUNet for segmenting lung mass from chest X-ray image. In Proceedings of the 2022 IEEE International Conference on Consumer Electronics-Taiwan. IEEE, 175\u2013176."},{"key":"e_1_3_1_93_2","unstructured":"Kaushik Dutta. 2021. Densely connected recurrent residual (dense R2UNet) convolutional neural network for segmentation of lung CT images. arXiv:2102.00663. Retrieved from https:\/\/arxiv.org\/abs\/2102.00663"},{"key":"e_1_3_1_94_2","doi-asserted-by":"crossref","unstructured":"Prasad Dutande Ujjwal Baid and Sanjay Talbar. 2021. LNCDS: A 2D-3D cascaded CNN approach for lung nodule classification detection and segmentation. Biomedical Signal Processing and Control 67 (2021) 102527.","DOI":"10.1016\/j.bspc.2021.102527"},{"key":"e_1_3_1_95_2","doi-asserted-by":"publisher","unstructured":"Evan Shelhamer Jonathan Long and Trevor Darrell. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 4 (2017) 640\u2013651. DOI:10.1109\/TPAMI.2016.2572683","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"e_1_3_1_96_2","unstructured":"Md Zahangir Alom Mahmudul Hasan Chris Yakopcic Tarek M. Taha and Vijayan K. Asari. 2018. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv:1802.06955. Retrieved from https:\/\/arxiv.org\/abs\/1802.06955"},{"key":"e_1_3_1_97_2","doi-asserted-by":"crossref","unstructured":"Zhaojin Fu Jinjiang Li and Zhen Hua. 2023. MSA-Net: Multiscale spatial attention network for medical image segmentation. Alexandria Engineering Journal 70 1 (2023) 453\u2013473.","DOI":"10.1016\/j.aej.2023.02.039"},{"key":"e_1_3_1_98_2","doi-asserted-by":"crossref","unstructured":"Mehmet Akif Cifci. 2022. SegChaNet: A novel model for lung cancer segmentation in CT scans. Applied Bionics and Biomechanics 2022 Article 1139587 (2022).","DOI":"10.1155\/2022\/1139587"},{"key":"e_1_3_1_99_2","doi-asserted-by":"crossref","unstructured":"Haojie Song Yuefei Wang Shijie Zeng Xiaoyan Guo and Zheheng Li. 2023. OAU-net: Outlined attention U-net for biomedical image segmentation. Biomedical Signal Processing and Control 79 (2023) 104038.","DOI":"10.1016\/j.bspc.2022.104038"},{"key":"e_1_3_1_100_2","doi-asserted-by":"crossref","unstructured":"Tao Zhou YaLi Dong HuiLing Lu XiaoMin Zheng Shi Qiu and SenBao Hou. 2022. APU-Net: An attention mechanism parallel U-net for lung tumor segmentation. BioMed Research International 2022 Article 5303651 (2022).","DOI":"10.1155\/2022\/5303651"},{"key":"e_1_3_1_101_2","doi-asserted-by":"crossref","unstructured":"Ran Gu Guotai Wang Tao Song Rui Huang Michael Aertsen Jan Deprest S\u00e9bastien Ourselin Tom Vercauteren and Shaoting Zhang. 2020. CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Transactions on Medical Imaging 40 2 (2020) 699\u2013711.","DOI":"10.1109\/TMI.2020.3035253"},{"key":"e_1_3_1_102_2","unstructured":"Xiaocong Chen Lina Yao and Yu Zhang. 2020. Residual attention U-Net for automated multi-class segmentation of COVID-19 chest CT images. arXiv:2004.05645. Retrieved from https:\/\/arxiv.org\/abs\/2004.05645"},{"key":"e_1_3_1_103_2","doi-asserted-by":"crossref","unstructured":"Zongwei Zhou Md Mahfuzur Rahman Siddiquee Nima Tajbakhsh and Jianming Liang. 2020. UNet++: A nested U-Net architecture for medical image segmentation. IEEE Transactions on Medical Imaging 39 6 (2020) 1856\u20131867.","DOI":"10.1109\/TMI.2019.2959609"},{"key":"e_1_3_1_104_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_3_1_105_2","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1007\/978-3-031-34127-4_44","volume-title":"Current Problems in Applied Mathematics and Computer Science and Systems","author":"Gautam Nandita","year":"2023","unstructured":"Nandita Gautam, Abhishek Basu, Dmitry Kaplun, and Ram Sarkar. 2023. An ensemble of UNet frameworks for lung nodule segmentation. In Current Problems in Applied Mathematics and Computer Science and Systems. Anatoly Alikhanov, Pavel Lyakhov, and Irina Samoylenko (Eds.), Springer Nature Switzerland, Cham, 450\u2013461."},{"key":"e_1_3_1_106_2","doi-asserted-by":"crossref","unstructured":"Yang Xu Shike Hou Xiangyu Wang Duo Li and Lu Lu. 2023. A medical image segmentation method based on improved UNet 3+ network. Diagnostics 13 3 (2023) 576.","DOI":"10.3390\/diagnostics13030576"},{"key":"e_1_3_1_107_2","doi-asserted-by":"crossref","unstructured":"Zezhi Wu Xiaoshu Li and Jianhui Zuo. 2023. RAD-UNet: Research on an improved lung nodule semantic segmentation algorithm based on deep learning. Frontiers in Oncology 13 (2023) 1084096.","DOI":"10.3389\/fonc.2023.1084096"},{"key":"e_1_3_1_108_2","doi-asserted-by":"publisher","unstructured":"Tong Wang Fubin Wu Haoran Lu and Shengzhou Xu. 2023. CA-UNet: Convolution and attention fusion for lung nodule segmentation. International Journal of Imaging Systems and Technology 33 5 (2023) 1469\u20131479. DOI:10.1002\/ima.22878","DOI":"10.1002\/ima.22878"},{"key":"e_1_3_1_109_2","doi-asserted-by":"publisher","unstructured":"Baihua Zhang Shouliang Qi Yanan Wu Xiaohuan Pan Yudong Yao Wei Qian and Yubao Guan. 2022. Multi-scale segmentation squeeze-and-excitation UNet with conditional random field for segmenting lung tumor from CT images. Computer Methods and Programs in Biomedicine 222 (2022) 106946. DOI:10.1016\/j.cmpb.2022.106946","DOI":"10.1016\/j.cmpb.2022.106946"},{"key":"e_1_3_1_110_2","doi-asserted-by":"publisher","unstructured":"Guobin Zhang Zhiyong Yang and Shan Jiang. 2022. Automatic lung tumor segmentation from CT images using improved 3D densely connected UNet. Medical & Biological Engineering & Computing 60 11 (2022) 3311\u20133323. DOI:10.1007\/s11517-022-02667-0","DOI":"10.1007\/s11517-022-02667-0"},{"key":"e_1_3_1_111_2","doi-asserted-by":"crossref","unstructured":"Lei Yang Yuge Gu Benyan Huo Yanhong Liu and Guibin Bian. 2022. A shape-guided deep residual network for automated CT lung segmentation. Knowledge-Based Systems 250 (2022) 108981.","DOI":"10.1016\/j.knosys.2022.108981"},{"key":"e_1_3_1_112_2","first-page":"141","volume-title":"Proceedings of the 2021 31st International Conference on Computer Theory and Applications (ICCTA)","author":"Salama Wessam M.","year":"2021","unstructured":"Wessam M. Salama and Moustafa H. Aly. 2021. Lung CT image segmentation: A generalized framework based on U-net architecture and preprocessing models. In Proceedings of the 2021 31st International Conference on Computer Theory and Applications (ICCTA). IEEE, 141\u2013146."},{"key":"e_1_3_1_113_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICACCS51430.2021.9441977"},{"key":"e_1_3_1_114_2","doi-asserted-by":"publisher","unstructured":"Wei Chen Fengchang Yang Xianru Zhang Xin Xu and Xu Qiao. 2021. MAU-Net: Multiple attention 3D U-Net for lung cancer segmentation on CT images. Procedia Computer Science 192 (2021) 543\u2013552. DOI:10.1016\/j.procs.2021.08.056Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 25th International Conference KES2021.","DOI":"10.1016\/j.procs.2021.08.056"},{"key":"e_1_3_1_115_2","doi-asserted-by":"publisher","unstructured":"Kuan bing Chen Ying Xuan Ai jun Lin and Shao hua Guo. 2021. Lung computed tomography image segmentation based on U-Net network fused with dilated convolution. Computer Methods and Programs in Biomedicine 207 (2021) 106170. DOI:10.1016\/j.cmpb.2021.106170","DOI":"10.1016\/j.cmpb.2021.106170"},{"key":"e_1_3_1_116_2","doi-asserted-by":"publisher","unstructured":"Zhitao Xiao Bowen Liu Lei Geng Fang Zhang and Yanbei Liu. 2020. Segmentation of lung nodules using improved 3D-UNet neural network. Symmetry 12 11 Article 1787 (2020). DOI:10.3390\/sym12111787","DOI":"10.3390\/sym12111787"},{"key":"e_1_3_1_117_2","doi-asserted-by":"publisher","unstructured":"Yeganeh Jalali Mansoor Fateh Mohsen Rezvani Vahid Abolghasemi and Mohammad Hossein Anisi. 2021. ResBCDU-Net: A deep learning framework for lung CT image segmentation. Sensors 21 1 (2021). DOI:10.3390\/s21010268","DOI":"10.3390\/s21010268"},{"key":"e_1_3_1_118_2","doi-asserted-by":"publisher","unstructured":"Joana Rocha Ant\u00f3nio Cunha and Ana Maria Mendon\u00e7a. 2020. Conventional filtering versus U-net based models for pulmonary nodule segmentation in CT images. Journal of Medical Systems 44 4 (2020) 81. DOI:10.1007\/s10916-020-1541-9","DOI":"10.1007\/s10916-020-1541-9"},{"key":"e_1_3_1_119_2","doi-asserted-by":"publisher","unstructured":"Muhammad Usman Byoung-Dai Lee Shi-Sub Byon Sung-Hyun Kim Byung-il Lee and Yeong-Gil Shin. 2020. Volumetric lung nodule segmentation using adaptive ROI with multi-view residual learning. Scientific Reports 10 1 (2020) 12839. DOI:10.1038\/s41598-020-69817-y","DOI":"10.1038\/s41598-020-69817-y"},{"key":"e_1_3_1_120_2","doi-asserted-by":"publisher","unstructured":"Guofeng Tong Yong Li Huairong Chen Qingchun Zhang and Huiying Jiang. 2018. Improved U-NET network for pulmonary nodules segmentation. Optik 174 (2018) 460\u2013469. DOI:10.1016\/j.ijleo.2018.08.086","DOI":"10.1016\/j.ijleo.2018.08.086"},{"key":"e_1_3_1_121_2","first-page":"153","volume-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Tan Zimeng","year":"2021","unstructured":"Zimeng Tan, Jianjiang Feng, and Jie Zhou. 2021. SGNet: Structure-aware graph-based network for airway semantic segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 153\u2013163."},{"key":"e_1_3_1_122_2","first-page":"600","volume-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Gaggion Nicol\u00e1s","year":"2021","unstructured":"Nicol\u00e1s Gaggion, Lucas Mansilla, Diego H. Milone, and Enzo Ferrante. 2021. Hybrid graph convolutional neural networks for landmark-based anatomical segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 600\u2013610."},{"key":"e_1_3_1_123_2","first-page":"36","volume-title":"Proceedings of the International Workshop on Graph Learning in Medical Imaging","author":"Zhai Zhiwei","year":"2019","unstructured":"Zhiwei Zhai, Marius Staring, Xuhui Zhou, Qiuxia Xie, Xiaojuan Xiao, M. Els Bakker, Lucia J. Kroft, Boudewijn P. F. Lelieveldt, Gudula JAM Boon, Frederikus A. Klok, et\u00a0al. 2019. Linking convolutional neural networks with graph convolutional networks: Application in pulmonary artery-vein separation. In Proceedings of the International Workshop on Graph Learning in Medical Imaging. Springer, 36\u201343."},{"key":"e_1_3_1_124_2","doi-asserted-by":"crossref","unstructured":"Jie Lian Yonghao Long Fan Huang Kei Shing Ng Faith M. Y. Lee David C. L. Lam Benjamin X. L. Fang Qi Dou and Varut Vardhanabhuti. 2022. Imaging-based deep graph neural networks for survival analysis in early stage lung cancer using CT: A multicenter study. Frontiers in Oncology 12 (2022) 868186.","DOI":"10.3389\/fonc.2022.868186"},{"key":"e_1_3_1_125_2","unstructured":"Haozhe Jia Haoteng Tang Guixiang Ma Weidong Cai Heng Huang Liang Zhan and Yong Xia. 2021. PSGR: Pixel-wise sparse graph reasoning for COVID-19 pneumonia segmentation in CT images. arXiv:2108.03809. Retrieved from https:\/\/arxiv.org\/abs\/2108.03809"},{"key":"e_1_3_1_126_2","doi-asserted-by":"crossref","unstructured":"Marc\u2019Aurelio Ranzato Christopher Poultney Sumit Chopra and Yann Cun. 2006. Efficient learning of sparse representations with an energy-based model. Advances in Neural Information Processing Systems 19 (2006).","DOI":"10.7551\/mitpress\/7503.003.0147"},{"key":"e_1_3_1_127_2","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"e_1_3_1_128_2","first-page":"833","volume-title":"Proceedings of the 28th International Conference on Machine Learning","author":"Salah Rifai","year":"2011","unstructured":"Rifai Salah, P. Vincent, and X. Muller. 2011. Contractive auto-encoders: Explicit invariance during feature extraction. In Proceedings of the 28th International Conference on Machine Learning. 833\u2013840."},{"key":"e_1_3_1_129_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21735-7_7"},{"key":"e_1_3_1_130_2","unstructured":"Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv:1312.6114. Retrieved from https:\/\/arxiv.org\/abs\/1312.6114"},{"key":"e_1_3_1_131_2","first-page":"843","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Srivastava Nitish","year":"2015","unstructured":"Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov. 2015. Unsupervised learning of video representations using lstms. In Proceedings of the International Conference on Machine Learning. PMLR, 843\u2013852."},{"key":"e_1_3_1_132_2","unstructured":"Alireza Makhzani Jonathon Shlens Navdeep Jaitly Ian Goodfellow and Brendan Frey. 2015. Adversarial autoencoders. arXiv:1511.05644. Retrieved from https:\/\/arxiv.org\/abs\/1511.05644"},{"key":"e_1_3_1_133_2","doi-asserted-by":"crossref","unstructured":"Ihsan Ullah Farman Ali Babar Shah Shaker El-Sappagh Tamer Abuhmed and Sang Hyun Park. 2023. A deep learning based dual encoder\u2013decoder framework for anatomical structure segmentation in chest X-ray images. Scientific Reports 13 1 (2023) 1\u201314.","DOI":"10.1038\/s41598-023-27815-w"},{"key":"e_1_3_1_134_2","doi-asserted-by":"crossref","unstructured":"Shichao Luo Jina Zhang Ning Xiao Yan Qiang Keqin Li Juanjuan Zhao Liang Meng and Ping Song. 2022. DAS-Net: A lung nodule segmentation method based on adaptive dual-branch attention and shadow mapping. Applied Intelligence 52 10 (2022) 15617\u201315631.","DOI":"10.1007\/s10489-021-03038-2"},{"key":"e_1_3_1_135_2","doi-asserted-by":"publisher","unstructured":"Abbas Khan Hyongsuk Kim and Leon Chua. 2021. PMED-Net: Pyramid based multi-scale encoder-decoder network for medical image segmentation. IEEE Access 9 (2021) 55988\u201355998. DOI:10.1109\/ACCESS.2021.3071754","DOI":"10.1109\/ACCESS.2021.3071754"},{"key":"e_1_3_1_136_2","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/978-3-031-12053-4_26","volume-title":"Proceedings of the Annual Conference on Medical Image Understanding and Analysis","author":"Ibrahim Samar","year":"2022","unstructured":"Samar Ibrahim, Kareem Elgohary, Mahmoud Higazy, Thanaa Mohannad, Sahar Selim, and Mustafa Elattar. 2022. Lung segmentation using resunet++ powered by variational auto encoder-based enhancement in chest X-ray images. In Proceedings of the Annual Conference on Medical Image Understanding and Analysis. Springer, 339\u2013356."},{"key":"e_1_3_1_137_2","doi-asserted-by":"crossref","unstructured":"Amitava Halder and Debangshu Dey. 2023. Atrous convolution aided integrated framework for lung nodule segmentation and classification. Biomedical Signal Processing and Control 82 (2023) 104527.","DOI":"10.1016\/j.bspc.2022.104527"},{"key":"e_1_3_1_138_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219944"},{"key":"e_1_3_1_139_2","first-page":"1348","volume-title":"Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Hossain Shahruk","year":"2019","unstructured":"Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad R. Abdullah, and M. Ariful Haque. 2019. A pipeline for lung tumor detection and segmentation from CT scans using dilated convolutional neural networks. In Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1348\u20131352."},{"key":"e_1_3_1_140_2","doi-asserted-by":"crossref","unstructured":"Haiying Xia Weifan Sun Shuxiang Song and Xiangwei Mou. 2020. Md-net: Multi-scale dilated convolution network for CT images segmentation. Neural Processing Letters 51 3 (2020) 2915\u20132927.","DOI":"10.1007\/s11063-020-10230-x"},{"key":"e_1_3_1_141_2","doi-asserted-by":"crossref","unstructured":"Lei Geng Siqi Zhang Jun Tong and Zhitao Xiao. 2019. Lung segmentation method with dilated convolution based on VGG-16 network. Computer Assisted Surgery 24 sup2 (2019) 27\u201333.","DOI":"10.1080\/24699322.2019.1649071"},{"key":"e_1_3_1_142_2","first-page":"1","volume-title":"2019 Digital Image Computing: Techniques and Applications (DICTA)","author":"Rasyidi Hanif","year":"2019","unstructured":"Hanif Rasyidi and Salman Khan. 2019. Historical document text binarization using atrous convolution and multi-scale feature decoder. In 2019 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 1\u20138."},{"key":"e_1_3_1_143_2","doi-asserted-by":"crossref","unstructured":"Mourad Gridach. 2021. PyDiNet: Pyramid dilated network for medical image segmentation. Neural Networks 140 (2021) 274\u2013281.","DOI":"10.1016\/j.neunet.2021.03.023"},{"key":"e_1_3_1_144_2","unstructured":"Liang-Chieh Chen George Papandreou Florian Schroff and Hartwig Adam. 2017. Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587. Retrieved from https:\/\/arxiv.org\/abs\/1706.05587"},{"key":"e_1_3_1_145_2","doi-asserted-by":"crossref","unstructured":"Liang-Chieh Chen George Papandreou Iasonas Kokkinos Kevin Murphy and Alan L. Yuille. 2017. Deeplab: Semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40 4 (2017) 834\u2013848.","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"e_1_3_1_146_2","doi-asserted-by":"crossref","unstructured":"Anushikha Singh Brejesh Lall Bijaya K. Panigrahi Anjali Agrawal Anurag Agrawal Balamugesh Thangakunam and D. J. Christopher. 2021. Deep LF-Net: Semantic lung segmentation from Indian chest radiographs including severely unhealthy images. Biomedical Signal Processing and Control 68 (2021) 102666.","DOI":"10.1016\/j.bspc.2021.102666"},{"key":"e_1_3_1_147_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/978-3-658-29267-6_17","volume-title":"Bildverarbeitung f\u00fcr die Medizin 2020: Algorithmen\u2013Systeme\u2013Anwendungen. Proceedings des Workshops vom 15. bis 17. M\u00e4rz 2020 in Berlin","author":"Chang Ching-Sheng","year":"2020","unstructured":"Ching-Sheng Chang, Jin-Fa Lin, Ming-Ching Lee, and Christoph Palm. 2020. Semantic lung segmentation using convolutional neural networks. In Bildverarbeitung f\u00fcr die Medizin 2020: Algorithmen\u2013Systeme\u2013Anwendungen. Proceedings des Workshops vom 15. bis 17. M\u00e4rz 2020 in Berlin. Springer, 75\u201380."},{"key":"e_1_3_1_148_2","doi-asserted-by":"crossref","unstructured":"N. Venugopal. 2020. Automatic semantic segmentation with DeepLab dilated learning network for change detection in remote sensing images. Neural Processing Letters 51 3 (2020) 2355\u20132377.","DOI":"10.1007\/s11063-019-10174-x"},{"key":"e_1_3_1_149_2","doi-asserted-by":"crossref","unstructured":"Xiangyu Zhao Peng Zhang Fan Song Guangda Fan Yangyang Sun Yujia Wang Zheyuan Tian Luqi Zhang and Guanglei Zhang. 2021. D2A U-Net: Automatic segmentation of COVID-19 CT slices based on dual attention and hybrid dilated convolution. Computers in Biology and Medicine 135 (2021) 104526.","DOI":"10.1016\/j.compbiomed.2021.104526"},{"key":"e_1_3_1_150_2","doi-asserted-by":"publisher","unstructured":"Mian Muhammad Naeem Abid Tehseen Zia Mubeen Ghafoor and David Windridge. 2021. Multi-view convolutional recurrent neural networks for lung cancer nodule identification. Neurocomputing 453 (2021) 299\u2013311. DOI:10.1016\/j.neucom.2020.06.144","DOI":"10.1016\/j.neucom.2020.06.144"},{"key":"e_1_3_1_151_2","doi-asserted-by":"crossref","unstructured":"S. Akila Agnes J. Anitha and A. Arun Solomon. 2022. Two-stage lung nodule detection framework using enhanced UNet and convolutional LSTM networks in CT images. Computers in Biology and Medicine 149 (2022) 106059.","DOI":"10.1016\/j.compbiomed.2022.106059"},{"key":"e_1_3_1_152_2","doi-asserted-by":"crossref","unstructured":"Ping Xuan Bin Jiang Hui Cui Qiangguo Jin Peng Cheng Toshiya Nakaguchi Tiangang Zhang Changyang Li Zhiyu Ning Menghan Guo et\u00a0al. 2022. Convolutional bi-directional learning and spatial enhanced attentions for lung tumor segmentation. Computer Methods and Programs in Biomedicine 226 (2022) 107147.","DOI":"10.1016\/j.cmpb.2022.107147"},{"key":"e_1_3_1_153_2","doi-asserted-by":"crossref","unstructured":"Yeganeh Jalali Mansoor Fateh Mohsen Rezvani Vahid Abolghasemi and Mohammad Hossein Anisi. 2021. ResBCDU-Net: A deep learning framework for lung CT image segmentation. Sensors 21 1 (2021) 268.","DOI":"10.3390\/s21010268"},{"key":"e_1_3_1_154_2","unstructured":"Maryam Asadi-Aghbolaghi Reza Azad Mahmood Fathy and Sergio Escalera. 2020. Multi-level context gating of embedded collective knowledge for medical image segmentation. arXiv:2003.05056. Retrieved from https:\/\/arxiv.org\/abs\/2003.05056"},{"key":"e_1_3_1_155_2","doi-asserted-by":"crossref","unstructured":"Vinit Kumar Gunjan Ninni Singh Fahimudin Shaik and Sudipta Roy. 2022. Detection of lung cancer in CT scans using grey wolf optimization algorithm and recurrent neural network. Health and Technology 12 6 (2022) 1197\u20131210.","DOI":"10.1007\/s12553-022-00700-8"},{"key":"e_1_3_1_156_2","doi-asserted-by":"crossref","unstructured":"M. Kanipriya C. Hemalatha N. Sridevi S. R. SriVidhya and S. L. Jany Shabu. 2022. An improved capuchin search algorithm optimized hybrid CNN-LSTM architecture for malignant lung nodule detection. Biomedical Signal Processing and Control 78 (2022) 103973.","DOI":"10.1016\/j.bspc.2022.103973"},{"key":"e_1_3_1_157_2","doi-asserted-by":"crossref","unstructured":"Shuaijing Xu Junqi Guo Guangzhi Zhang and Rongfang Bie. 2020. Automated detection of multiple lesions on chest x-ray images: Classification using a neural network technique with association-specific contexts. Applied Sciences 10 5 (2020) 1742.","DOI":"10.3390\/app10051742"},{"key":"e_1_3_1_158_2","doi-asserted-by":"crossref","unstructured":"Riqiang Gao Yucheng Tang Kaiwen Xu Yuankai Huo Shunxing Bao Sanja L. Antic Emily S. Epstein Steve Deppen Alexis B. Paulson Kim L. Sandler et\u00a0al. 2020. Time-distanced gates in long short-term memory networks. Medical Image Analysis 65 (2020) 101785.","DOI":"10.1016\/j.media.2020.101785"},{"key":"e_1_3_1_159_2","doi-asserted-by":"crossref","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2020. Generative adversarial networks. Communications of the ACM 63 11 (2020) 139\u2013144.","DOI":"10.1145\/3422622"},{"key":"e_1_3_1_160_2","first-page":"175","volume-title":"Proceedings of the 2019 International Conference on Image and Video Processing, and Artificial Intelligence","volume":"11321","author":"Cai Jiaxin","year":"2019","unstructured":"Jiaxin Cai and Hongfeng Zhu. 2019. Lung image segmentation by generative adversarial networks. In Proceedings of the 2019 International Conference on Image and Video Processing, and Artificial Intelligence, Vol. 11321. SPIE, 175\u2013180."},{"key":"e_1_3_1_161_2","doi-asserted-by":"crossref","unstructured":"Jiaxing Tan Longlong Jing Yumei Huo Lihong Li Oguz Akin and Yingli Tian. 2021. LGAN: Lung segmentation in CT scans using generative adversarial network. Computerized Medical Imaging and Graphics 87 (2021) 101817.","DOI":"10.1016\/j.compmedimag.2020.101817"},{"key":"e_1_3_1_162_2","doi-asserted-by":"crossref","unstructured":"Faizan Munawar Shoaib Azmat Talha Iqbal Christer Gr\u00f6nlund and Hazrat Ali. 2020. Segmentation of lungs in chest X-ray image using generative adversarial networks. IEEE Access 8 (2020) 153535\u2013153545.","DOI":"10.1109\/ACCESS.2020.3017915"},{"key":"e_1_3_1_163_2","doi-asserted-by":"crossref","unstructured":"Swati P. Pawar and Sanjay N. Talbar. 2021. LungSeg-Net: Lung field segmentation using generative adversarial network. Biomedical Signal Processing and Control 64 (2021) 102296.","DOI":"10.1016\/j.bspc.2020.102296"},{"key":"e_1_3_1_164_2","doi-asserted-by":"crossref","unstructured":"Shweta Tyagi and Sanjay N. Talbar. 2022. CSE-GAN: A 3D conditional generative adversarial network with concurrent squeeze-and-excitation blocks for lung nodule segmentation. Computers in Biology and Medicine 147 (2022) 105781.","DOI":"10.1016\/j.compbiomed.2022.105781"},{"key":"e_1_3_1_165_2","doi-asserted-by":"crossref","unstructured":"Mizuho Nishio Koji Fujimoto Hidetoshi Matsuo Chisako Muramatsu Ryo Sakamoto and Hiroshi Fujita. 2021. Lung cancer segmentation with transfer learning: Usefulness of a pretrained model constructed from an artificial dataset generated using a generative adversarial network. Frontiers in Artificial Intelligence 4 (2021) 694815.","DOI":"10.3389\/frai.2021.694815"},{"key":"e_1_3_1_166_2","doi-asserted-by":"crossref","unstructured":"Supiksha Jain Sanjeev Indora and Dinesh Kumar Atal. 2021. Lung nodule segmentation using salp shuffled shepherd optimization algorithm-based generative adversarial network. Computers in Biology and Medicine 137 (2021) 104811.","DOI":"10.1016\/j.compbiomed.2021.104811"},{"key":"e_1_3_1_167_2","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2018.8621451"},{"key":"e_1_3_1_168_2","doi-asserted-by":"publisher","unstructured":"Zeyu Gao Bangyang Hong Yang Li Xianli Zhang Jialun Wu Chunbao Wang Xiangrong Zhang Tieliang Gong Yefeng Zheng Deyu Meng et\u00a0al. 2023. A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images. Medical Image Analysis 83 (2023) 102652. DOI:10.1016\/j.media.2022.102652","DOI":"10.1016\/j.media.2022.102652"},{"key":"e_1_3_1_169_2","doi-asserted-by":"publisher","unstructured":"Yu Fu Peng Xue Taohui Xiao Zhili Zhang Youren Zhang and Enqing Dong. 2023. Semi-supervised adversarial learning for improving the diagnosis of pulmonary nodules. IEEE Journal of Biomedical and Health Informatics 27 1 (2023) 109\u2013120. DOI:10.1109\/JBHI.2022.3216446","DOI":"10.1109\/JBHI.2022.3216446"},{"key":"e_1_3_1_170_2","doi-asserted-by":"crossref","unstructured":"Vemund Fredriksen Svein Ole M. Sevle Andr\u00e9 Pedersen Thomas Lang\u00f8 Gabriel Kiss and Frank Lindseth. 2022. Teacher-student approach for lung tumor segmentation from mixed-supervised datasets. Plos One 17 4 (2022) e0266147.","DOI":"10.1371\/journal.pone.0266147"},{"key":"e_1_3_1_171_2","doi-asserted-by":"publisher","unstructured":"Feng Shi Bojiang Chen Qiqi Cao Ying Wei Qing Zhou Rui Zhang Yaojie Zhou Wenjie Yang Xiang Wang Rongrong Fan et\u00a0al. 2022. Semi-supervised deep transfer learning for benign-malignant diagnosis of pulmonary nodules in chest CT images. IEEE Transactions on Medical Imaging 41 4 (2022) 771\u2013781. DOI:10.1109\/TMI.2021.3123572","DOI":"10.1109\/TMI.2021.3123572"},{"key":"e_1_3_1_172_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2018.8512294"},{"key":"e_1_3_1_173_2","doi-asserted-by":"crossref","unstructured":"Evan Shelhamer Jonathan Long and Trevor Darrell. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 4 (2017) 640\u2013651.","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"e_1_3_1_174_2","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/978-3-319-67389-9_44","volume-title":"Machine Learning in Medical Imaging: 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings 8","author":"Salehi Seyed Sadegh Mohseni","year":"2017","unstructured":"Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, and Ali Gholipour. 2017. Tversky loss function for image segmentation using 3D fully convolutional deep networks. In Machine Learning in Medical Imaging: 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings 8. Springer, 379\u2013387."},{"key":"e_1_3_1_175_2","doi-asserted-by":"publisher","unstructured":"Manahil Zulfiqar Maciej Stanuch Marek Wodzinski and Andrzej Skalski. 2023. DRU-Net: Pulmonary artery segmentation via dense residual U-network with hybrid loss function. Sensors 23 12 (2023). DOI:10.3390\/s23125427","DOI":"10.3390\/s23125427"},{"key":"e_1_3_1_176_2","doi-asserted-by":"publisher","unstructured":"Giuseppe Pezzano Vicent Ribas Ripoll and Petia Radeva. 2021. CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation. Computer Methods and Programs in Biomedicine 198 (2021) 105792. DOI:10.1016\/j.cmpb.2020.105792","DOI":"10.1016\/j.cmpb.2020.105792"},{"key":"e_1_3_1_177_2","doi-asserted-by":"publisher","unstructured":"Yurou Sun Jinglei Tang Weijie Lei and Dongjian He. 2020. 3D segmentation of pulmonary nodules based on multi-view and semi-supervised. IEEE Access 8 (2020) 26457\u201326467. DOI:10.1109\/ACCESS.2020.2971542","DOI":"10.1109\/ACCESS.2020.2971542"},{"key":"e_1_3_1_178_2","doi-asserted-by":"publisher","unstructured":"Yutong Xie Jianpeng Zhang and Yong Xia. 2019. Semi-supervised adversarial model for benign\u2013malignant lung nodule classification on chest CT. Medical Image Analysis 57 (2019) 237\u2013248. DOI:10.1016\/j.media.2019.07.004","DOI":"10.1016\/j.media.2019.07.004"},{"key":"e_1_3_1_179_2","doi-asserted-by":"publisher","unstructured":"Min Chen Xiaobo Shi Yin Zhang Di Wu and Mohsen Guizani. 2021. Deep feature learning for medical image analysis with convolutional autoencoder neural network. IEEE Transactions on Big Data 7 4 (2021) 750\u2013758. DOI:10.1109\/TBDATA.2017.2717439","DOI":"10.1109\/TBDATA.2017.2717439"},{"key":"e_1_3_1_180_2","doi-asserted-by":"publisher","unstructured":"Tongxue Zhou Su Ruan and St\u00e9phane Canu. 2019. A review: Deep learning for medical image segmentation using multi-modality fusion. Array 3-4 (2019) 100004. DOI:10.1016\/j.array.2019.100004","DOI":"10.1016\/j.array.2019.100004"},{"key":"e_1_3_1_181_2","doi-asserted-by":"publisher","unstructured":"Yichi Zhang and Rushi Jiao. 2024. Towards segment anything model (SAM) for medical image segmentation: A survey. Elsevier 171 (2024) 108238. DOI:10.1016\/j.compbiomed.2024.108238","DOI":"10.1016\/j.compbiomed.2024.108238"},{"key":"e_1_3_1_182_2","doi-asserted-by":"crossref","unstructured":"Ebrahim Khalili Blanca Priego-Torres Antonio Leon-Jimenez and Daniel Sanchez-Morillo. 2024. Automatic lung segmentation in chest X-ray images using SAM with prompts from YOLO. TechRxiv 12 (2024) 122805\u2013122819.","DOI":"10.1109\/ACCESS.2024.3454188"},{"key":"e_1_3_1_183_2","doi-asserted-by":"publisher","unstructured":"Maciej A. Mazurowski Haoyu Dong Hanxue Gu Jichen Yang Nicholas Konz and Yixin Zhang. 2023. Segment anything model for medical image analysis: An experimental study. Medical Image Analysis 89 (2023) 102918. DOI:10.1016\/j.media.2023.102918","DOI":"10.1016\/j.media.2023.102918"},{"key":"e_1_3_1_184_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI56570.2024.10635844"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3797901","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T11:45:43Z","timestamp":1776512743000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3797901"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,18]]},"references-count":183,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,7,31]]}},"alternative-id":["10.1145\/3797901"],"URL":"https:\/\/doi.org\/10.1145\/3797901","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,18]]},"assertion":[{"value":"2023-07-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-22","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}