{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T13:49:36Z","timestamp":1743342576579,"version":"3.37.3"},"reference-count":26,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006190","name":"Research and Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006190","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,9]]},"DOI":"10.1109\/bibm52615.2021.9669868","type":"proceedings-article","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T20:40:30Z","timestamp":1642192830000},"page":"1119-1124","source":"Crossref","is-referenced-by-count":2,"title":["A Multi-Resolution Deep Forest Framework with Hybrid Feature Fusion for CT Whole Heart Segmentation"],"prefix":"10.1109","author":[{"given":"Fei","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingli","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dihan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingqi","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunhong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingqiang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingde","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinhuan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2923318"},{"journal-title":"Cfun Combining faster r-cnn and u-net network for efficient whole heart segmentation","year":"2018","author":"xu","key":"ref11"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10404-1_65"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"ref14","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"1","author":"krizhevsky","year":"2012","journal-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-75541-0_20"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-75541-0_24"},{"journal-title":"A two-stage 3d unet framework for multi-class segmentation on full resolution image","year":"2018","author":"wang","key":"ref18"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-75541-0_21"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.21.1.010901"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2020.00025"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101537"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1260\/2040-2295.4.3.371"},{"key":"ref8","first-page":"5538","article-title":"Automatic whole heart segmentation in ct images based on multi-atlas image registration","author":"yang","year":"2017"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.03.106"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1186\/1687-5281-2014-52"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-75541-0_22"},{"journal-title":"Institute for Health Metrics and Evaluation","year":"2019","key":"ref1"},{"key":"ref20","first-page":"242","article-title":"Automatic whole heart segmentation using deep learning and shape context. In Statistical Atlases and Computational Models of the Heart","author":"wang","year":"2018","journal-title":"ACDC and MMWHS Challenges"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2899635"},{"key":"ref21","first-page":"215","article-title":"Hybrid loss guided convolutional networks for whole heart parsing","author":"xin","year":"2018","journal-title":"Statistical Atlases and Computational Models of the Heart ACDC and MMWHS Challenges"},{"key":"ref24","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"Computer Science"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwy108"},{"key":"ref26","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","author":"ronneberger","year":"2015"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"}],"event":{"name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","start":{"date-parts":[[2021,12,9]]},"location":"Houston, TX, USA","end":{"date-parts":[[2021,12,12]]}},"container-title":["2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9669261\/9669139\/09669868.pdf?arnumber=9669868","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:57:15Z","timestamp":1652201835000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9669868\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,9]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/bibm52615.2021.9669868","relation":{},"subject":[],"published":{"date-parts":[[2021,12,9]]}}}