{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:29:32Z","timestamp":1753601372917,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation Program of China","award":["62071048, 81871374, 61971040, 62171039"],"award-info":[{"award-number":["62071048, 81871374, 61971040, 62171039"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,11,12]]},"DOI":"10.1145\/3502827.3502840","type":"proceedings-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T23:28:04Z","timestamp":1643326084000},"page":"90-96","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Localization and Classification of Coronary Artery Plaques from Cardiac CTA with A Boundary-Constrained 3D Fully Convolutional Network"],"prefix":"10.1145","author":[{"given":"Xinnian","family":"Yang","sequence":"first","affiliation":[{"name":"School of Optics and Photonics\/Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, Beijing Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Han","sequence":"additional","affiliation":[{"name":"School of Optics and Photonics\/Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, Beijing Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruirui","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Optics and Photonics\/Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, Beijing Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingfan","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Optics and Photonics\/Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, Beijing Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danni","family":"Ai","sequence":"additional","affiliation":[{"name":"School of Optics and Photonics\/Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, Beijing Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,1,27]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"8","article-title":"Coronary Plaque Features on CTA Can\u00a0Identify Patients at Increased Risk of\u00a0Cardiovascular Events","volume":"13","author":"Andreini Daniele","year":"2020","unstructured":"Andreini Daniele 2020 . Coronary Plaque Features on CTA Can\u00a0Identify Patients at Increased Risk of\u00a0Cardiovascular Events . JACC: Cardiovascular Imaging. 13 , 8 (Aug. 2020), 1704\u20131717. DOI:https:\/\/doi.org\/10.1016\/j.jcmg.2019.06.019. 10.1016\/j.jcmg.2019.06.019 Andreini Daniele 2020. Coronary Plaque Features on CTA Can\u00a0Identify Patients at Increased Risk of\u00a0Cardiovascular Events. JACC: Cardiovascular Imaging. 13, 8 (Aug. 2020), 1704\u20131717. DOI:https:\/\/doi.org\/10.1016\/j.jcmg.2019.06.019.","journal-title":"JACC: Cardiovascular Imaging."},{"key":"e_1_3_2_1_2_1","volume-title":"Computerized Medical Imaging and Graphics. 83","author":"Candemir S.","year":"2020","unstructured":"Candemir , S. , White , R.D. , Demirer , M. , Gupta , V. , Bigelow , M.T. , Prevedello , L.M. and Erdal , B.S . 2020. Automated coronary artery atherosclerosis detection and weakly supervised localization on coronary CT angiography with a deep 3-dimensional convolutional neural network . Computerized Medical Imaging and Graphics. 83 , ( Jul. 2020 ), 101721. DOI:https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101721. 10.1016\/j.compmedimag.2020.101721 Candemir, S., White, R.D., Demirer, M., Gupta, V., Bigelow, M.T., Prevedello, L.M. and Erdal, B.S. 2020. Automated coronary artery atherosclerosis detection and weakly supervised localization on coronary CT angiography with a deep 3-dimensional convolutional neural network. Computerized Medical Imaging and Graphics. 83, (Jul. 2020), 101721. DOI:https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101721."},{"key":"#cr-split#-e_1_3_2_1_3_1.1","doi-asserted-by":"crossref","unstructured":"Chicco D. and Jurman G. 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics. 21 1 (Jan. 2020) 6. DOI:https:\/\/doi.org\/10.1186\/s12864-019-6413-7. 10.1186\/s12864-019-6413-7","DOI":"10.1186\/s12864-019-6413-7"},{"key":"#cr-split#-e_1_3_2_1_3_1.2","doi-asserted-by":"crossref","unstructured":"Chicco D. and Jurman G. 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics. 21 1 (Jan. 2020) 6. DOI:https:\/\/doi.org\/10.1186\/s12864-019-6413-7.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/0146-664X(80)90054-4"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-37168-4"},{"key":"e_1_3_2_1_6_1","first-page":"8","article-title":"Computerized detection of noncalcified plaques in coronary CT angiography: evaluation of topological soft gradient prescreening method and luminal analysis","volume":"41","author":"Hp C., A, C., P, A., J, K., L, H., S, P.","year":"2014","unstructured":"J, W., C, Z., Hp , C., A, C., P, A., J, K., L, H., S, P. and E, K. 2014 . Computerized detection of noncalcified plaques in coronary CT angiography: evaluation of topological soft gradient prescreening method and luminal analysis . Medical Physics. 41 , 8 (Aug. 2014), 081901\u2013081901. DOI:https:\/\/doi.org\/10.1118\/1.4885958. 10.1118\/1.4885958 J, W., C, Z., Hp, C., A, C., P, A., J, K., L, H., S, P. and E, K. 2014. Computerized detection of noncalcified plaques in coronary CT angiography: evaluation of topological soft gradient prescreening method and luminal analysis. Medical Physics. 41, 8 (Aug. 2014), 081901\u2013081901. DOI:https:\/\/doi.org\/10.1118\/1.4885958.","journal-title":"Medical Physics."},{"key":"e_1_3_2_1_7_1","volume-title":"Computers in Biology and Medicine. 89","author":"Jawaid M.M.","year":"2017","unstructured":"Jawaid , M.M. , Riaz , A. , Rajani , R. , Reyes-Aldasoro , C.C. and Slabaugh , G . 2017. Framework for detection and localization of coronary non-calcified plaques in cardiac CTA using mean radial profiles . Computers in Biology and Medicine. 89 , ( Oct. 2017 ), 84\u201395. DOI:https:\/\/doi.org\/10.1016\/j.compbiomed.2017.07.021. 10.1016\/j.compbiomed.2017.07.021 Jawaid, M.M., Riaz, A., Rajani, R., Reyes-Aldasoro, C.C. and Slabaugh, G. 2017. Framework for detection and localization of coronary non-calcified plaques in cardiac CTA using mean radial profiles. Computers in Biology and Medicine. 89, (Oct. 2017), 84\u201395. DOI:https:\/\/doi.org\/10.1016\/j.compbiomed.2017.07.021."},{"key":"e_1_3_2_1_8_1","volume-title":"International Conference on Medical Imaging with Deep Learning (May","author":"Kervadec H.","year":"2019","unstructured":"Kervadec , H. , Bouchtiba , J. , Desrosiers , C. , Granger , E. , Dolz , J. and Ayed , I.B . 2019. Boundary loss for highly unbalanced segmentation . International Conference on Medical Imaging with Deep Learning (May 2019 ), 285\u2013296. Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, E., Dolz, J. and Ayed, I.B. 2019. Boundary loss for highly unbalanced segmentation. International Conference on Medical Imaging with Deep Learning (May 2019), 285\u2013296."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-12029-0_15"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the Third Conference on Medical Imaging with Deep Learning (Jul.","author":"Ma J.","year":"2020","unstructured":"Ma , J. , Wei , Z. , Zhang , Y. , Wang , Y. , Lv , R. , Zhu , C. , Gaoxiang , C. , Liu , J. , Peng , C. , Wang , L. , Wang , Y. and Chen , J . 2020. How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study . Proceedings of the Third Conference on Medical Imaging with Deep Learning (Jul. 2020 ), 479\u2013492. Ma, J., Wei, Z., Zhang, Y., Wang, Y., Lv, R., Zhu, C., Gaoxiang, C., Liu, J., Peng, C., Wang, L., Wang, Y. and Chen, J. 2020. How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study. Proceedings of the Third Conference on Medical Imaging with Deep Learning (Jul. 2020), 479\u2013492."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehy394"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atherosclerosis.2011.07.005"},{"key":"#cr-split#-e_1_3_2_1_14_1.1","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Garc\u00eda F. Sparks R. and Ourselin S. 2021. TorchIO: A Python library for efficient loading preprocessing augmentation and patch-based sampling of medical images in deep learning. Computer Methods and Programs in Biomedicine. 208 (Sep. 2021) 106236. DOI:https:\/\/doi.org\/10.1016\/j.cmpb.2021.106236. 10.1016\/j.cmpb.2021.106236","DOI":"10.1016\/j.cmpb.2021.106236"},{"key":"#cr-split#-e_1_3_2_1_14_1.2","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Garc\u00eda F. Sparks R. and Ourselin S. 2021. TorchIO: A Python library for efficient loading preprocessing augmentation and patch-based sampling of medical images in deep learning. Computer Methods and Programs in Biomedicine. 208 (Sep. 2021) 106236. DOI:https:\/\/doi.org\/10.1016\/j.cmpb.2021.106236.","DOI":"10.1016\/j.cmpb.2021.106236"},{"key":"#cr-split#-e_1_3_2_1_15_1.1","doi-asserted-by":"crossref","unstructured":"Rajendra Acharya U. Meiburger K.M. Wei Koh J.E. Vicnesh J. Ciaccio E.J. Shu Lih O. Tan S.K. Aman R.R.A.R. Molinari F. and Ng K.H. 2019. Automated plaque classification using computed tomography angiography and Gabor transformations. Artificial Intelligence in Medicine. 100 (Sep. 2019) 101724. DOI:https:\/\/doi.org\/10.1016\/j.artmed.2019.101724. 10.1016\/j.artmed.2019.101724","DOI":"10.1016\/j.artmed.2019.101724"},{"key":"#cr-split#-e_1_3_2_1_15_1.2","doi-asserted-by":"crossref","unstructured":"Rajendra Acharya U. Meiburger K.M. Wei Koh J.E. Vicnesh J. Ciaccio E.J. Shu Lih O. Tan S.K. Aman R.R.A.R. Molinari F. and Ng K.H. 2019. Automated plaque classification using computed tomography angiography and Gabor transformations. Artificial Intelligence in Medicine. 100 (Sep. 2019) 101724. DOI:https:\/\/doi.org\/10.1016\/j.artmed.2019.101724.","DOI":"10.1016\/j.artmed.2019.101724"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Ronneberger O. Fischer P. and Brox T. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015 (Cham 2015) 234\u2013241.  Ronneberger O. Fischer P. and Brox T. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015 (Cham 2015) 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.14.13760"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2899534"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2412651"},{"key":"e_1_3_2_1_20_1","volume-title":"Information Fusion. 71","author":"Zhang W.","year":"2021","unstructured":"Zhang , W. , Yang , G. , Zhang , N. , Xu , L. , Wang , X. , Zhang , Y. , Zhang , H. , Del Ser , J. and de Albuquerque, V.H.C. 2021. Multi-task learning with Multi-viewWeighted Fusion Attention for artery-specific calcification analysis . Information Fusion. 71 , ( Jul. 2021 ), 64\u201376. DOI:https:\/\/doi.org\/10.1016\/j.inffus.2021.01.009. 10.1016\/j.inffus.2021.01.009 Zhang, W., Yang, G., Zhang, N., Xu, L., Wang, X., Zhang, Y., Zhang, H., Del Ser, J. and de Albuquerque, V.H.C. 2021. Multi-task learning with Multi-viewWeighted Fusion Attention for artery-specific calcification analysis. Information Fusion. 71, (Jul. 2021), 64\u201376. DOI:https:\/\/doi.org\/10.1016\/j.inffus.2021.01.009."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-018-1880-6"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2883807"}],"event":{"name":"ICAIP 2021: 2021 5th International Conference on Advances in Image Processing","acronym":"ICAIP 2021","location":"Chengdu China"},"container-title":["2021 5th International Conference on Advances in Image Processing (ICAIP)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3502827.3502840","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3502827.3502840","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:34Z","timestamp":1750188634000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3502827.3502840"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,12]]},"references-count":25,"alternative-id":["10.1145\/3502827.3502840","10.1145\/3502827"],"URL":"https:\/\/doi.org\/10.1145\/3502827.3502840","relation":{},"subject":[],"published":{"date-parts":[[2021,11,12]]},"assertion":[{"value":"2022-01-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}