{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:29:26Z","timestamp":1750220966639,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,8,24]],"date-time":"2019-08-24T00:00:00Z","timestamp":1566604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["61802335, 61973250"],"award-info":[{"award-number":["61802335, 61973250"]}]},{"name":"the Natural Science Foundation of Hebei Province of China","award":["F2018203096"],"award-info":[{"award-number":["F2018203096"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,8,24]]},"DOI":"10.1145\/3364836.3364844","type":"proceedings-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T13:04:52Z","timestamp":1577106292000},"page":"36-42","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Body Composition Segmentation Based on Dual-pathway Deep Dilated Convolutional Neural Network"],"prefix":"10.1145","author":[{"given":"Tiange","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China"}]},{"given":"Pengfei","family":"Xu","sequence":"additional","affiliation":[{"name":"Information Science and Technology School, Northwest University, Xi'an, China"}]},{"given":"Yuchang","family":"Bo","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China"}]}],"member":"320","published-online":{"date-parts":[[2019,8,24]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Duda K. Majerczak J. Nieckarz Z. Heymsfield S. B. Zoladz J. A. 2019. Human Body Composition and Muscle Mass Muscle and Exercise Physiology. Salt Lake City: Academic Press 3--26.  Duda K. Majerczak J. Nieckarz Z. Heymsfield S. B. Zoladz J. A. 2019. Human Body Composition and Muscle Mass Muscle and Exercise Physiology. Salt Lake City: Academic Press 3--26.","DOI":"10.1016\/B978-0-12-814593-7.00001-3"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2004-0395"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.106.675355"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nutres.2012.12.007"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamcollsurg.2013.01.007"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/ajcn\/80.2.271"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1177\/193229680800200623"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clnu.2011.12.011"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/ijo.2008.242"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1139\/H08-075"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1152\/japplphysiol.00744.2004"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.3302"},{"key":"e_1_3_2_1_13_1","first-page":"6","article-title":"Optimization of Abdominal Fat Quantification on CT Imaging Through Use of Standardized Anatomic Space","volume":"41","author":"Tong Y.","year":"2014","unstructured":"Tong , Y. , Udupa , J. K. , Torigian , D. A. 2014 . Optimization of Abdominal Fat Quantification on CT Imaging Through Use of Standardized Anatomic Space : A Novel Approach. Med Phys. 41 , 6 , 063501. Tong, Y., Udupa, J. K., Torigian, D. A. 2014. Optimization of Abdominal Fat Quantification on CT Imaging Through Use of Standardized Anatomic Space: A Novel Approach. Med Phys. 41, 6, 063501.","journal-title":"A Novel Approach. Med Phys."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1749-6632.2000.tb06416.x"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2012.45.2722"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.106.675355"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/sj.ijo.0803454"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Ronneberger O. Fischer P. Brox T. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation \" in Proc. MICCAI. 234--241.  Ronneberger O. Fischer P. Brox T. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation \" in Proc. MICCAI. 234--241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2553401"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2548501"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.004"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2524985"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Su H. Xing F. Kong X. etal 2015. Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders. Med Image Comput Comput Assist Interv. 383--390.  Su H. Xing F. Kong X. et al. 2015. Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders. Med Image Comput Comput Assist Interv. 383--390.","DOI":"10.1007\/978-3-319-24574-4_46"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.1273918"},{"key":"e_1_3_2_1_25_1","unstructured":"Yu F. Koltun V. 2015. Multi-Scale Context Aggregation by Dilated Convolutions. arXiv:1511.07122v2. https:\/\/arxiv.org\/abs\/1511.07122.  Yu F. Koltun V. 2015. Multi-Scale Context Aggregation by Dilated Convolutions. arXiv:1511.07122v2. https:\/\/arxiv.org\/abs\/1511.07122."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018181432"},{"key":"e_1_3_2_1_27_1","unstructured":"Shen N. Li X. Zheng S. Zhang L. Fu Y. Liu X. etal Automated and Accurate Quantification of Subcutaneous and Visceral Adipose Tissue from Magnetic Resonance Imaging based on Machine Learning. Magn. Reson. Imaging to be published.  Shen N. Li X. Zheng S. Zhang L. Fu Y. Liu X. et al. Automated and Accurate Quantification of Subcutaneous and Visceral Adipose Tissue from Magnetic Resonance Imaging based on Machine Learning. Magn. Reson. Imaging to be published."}],"event":{"name":"ISICDM 2019: The Third International Symposium on Image Computing and Digital Medicine","sponsor":["Xidian University"],"location":"Xi'an China","acronym":"ISICDM 2019"},"container-title":["Proceedings of the Third International Symposium on Image Computing and Digital Medicine"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3364836.3364844","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3364836.3364844","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:54:22Z","timestamp":1750204462000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3364836.3364844"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,24]]},"references-count":27,"alternative-id":["10.1145\/3364836.3364844","10.1145\/3364836"],"URL":"https:\/\/doi.org\/10.1145\/3364836.3364844","relation":{},"subject":[],"published":{"date-parts":[[2019,8,24]]},"assertion":[{"value":"2019-08-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}