PublisherDOIYearVolumeIssuePageTitleAuthor(s)Link
10.21203/rs.3.rs-92779/v12020Deep Learning Based Pectoral Muscle Segmentation on MIAS MammogramsYoung Jae Kim, Eun Young Yoo, Kwang Gi Kimhttps://www.researchsquare.com/article/rs-92779/v1, https://www.researchsquare.com/article/rs-92779/v1.html
IEEE Consumer Electronics Magazine10.1109/mce.2020.298679920209528-33Segmentation Masks for the Mini-Mammographic Image Analysis Society (mini-MIAS) DatabaseMario Mustra, Andrija Stajduharhttp://xplorestaging.ieee.org/ielx7/5962380/9158606/09158639.pdf?arnumber=9158639
IEEE Access10.1109/access.2020.303666220208204173-204182On Segmentation of Pectoral Muscle in Digital Mammograms by Means of Deep LearningHossein Soleimani, Oleg V. Michailovichhttp://xplorestaging.ieee.org/ielx7/6287639/8948470/09252130.pdf?arnumber=9252130
Pattern Recognition10.1016/j.patcog.2012.09.0212013463681-691Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviationYanfeng Li, Houjin Chen, Yongyi Yang, Na Yanghttps://api.elsevier.com/content/article/PII:S003132031200427X?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S003132031200427X?httpAccept=text/plain
Applied Mechanics and Materials10.4028/www.scientific.net/amm.121-126.45372011121-1264537-4541Pectoral Muscle Segmentation for Digital Mammograms Based on Otsu ThresholdingChen Chung Liu, Shyr Shen Yu, Chung Yen Tsai, Ta Shan Tsuihttps://www.scientific.net/AMM.121-126.4537.pdf
Medical Image Analysis10.1016/j.media.2019.06.0072019571-17Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection networkAndrik Rampun, Karen López-Linares, Philip J. Morrow, Bryan W. Scotney, Hui Wang, Inmaculada Garcia Ocaña, Grégory Maclair, Reyer Zwiggelaar, Miguel A. González Ballester, Iván Macíahttps://api.elsevier.com/content/article/PII:S1361841518301129?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S1361841518301129?httpAccept=text/plain
Medical Physics10.1118/1.339557620103752289-2299Computerized image analysis: Texture-field orientation method for pectoral muscle identification on MLO-view mammogramsChuan Zhou, Jun Wei, Heang-Ping Chan, Chintana Paramagul, Lubomir M. Hadjiiski, Berkman Sahiner, Julie A. Douglashttps://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1118%2F1.3395576, https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1118%2F1.3395576, https://onlinelibrary.wiley.com/doi/full/10.1118/1.3395576
IEEE Transactions on Medical Imaging10.1109/tmi.2004.83052920042391129-1140Automatic Pectoral Muscle Segmentation on Mediolateral Oblique View MammogramsS.M. Kwok, R. Chandrasekhar, Y. Attikiouzel, M.T. Rickardhttp://xplorestaging.ieee.org/ielx5/42/29364/01327692.pdf?arnumber=1327692
Artificial Intelligence in Medicine10.1016/j.artmed.2017.06.00120177928-41Fully automated breast boundary and pectoral muscle segmentation in mammogramsAndrik Rampun, Philip J. Morrow, Bryan W. Scotney, John Winderhttps://api.elsevier.com/content/article/PII:S0933365717301471?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S0933365717301471?httpAccept=text/plain
Brain Tumor MRI Image Segmentation Using Deep Learning Techniques10.1016/b978-0-323-91171-9.00011-92022215-225Comparative analysis of deformable models based segmentation methods for brain tumor classificationD. Jayadevappa, Subodh Ingaleshwar, Sharan Kumarhttps://api.elsevier.com/content/article/PII:B9780323911719000119?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:B9780323911719000119?httpAccept=text/plain