{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T21:15:08Z","timestamp":1765228508089,"version":"3.46.0"},"reference-count":67,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Science and Technology Innovation (STI) 2030\u2014Major Projects","award":["2021ZD0204500","2021ZD0204503"],"award-info":[{"award-number":["2021ZD0204500","2021ZD0204503"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32171461"],"award-info":[{"award-number":["32171461"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Youth Innovation Promotion Association of Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["2022133"],"award-info":[{"award-number":["2022133"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1109\/tmi.2025.3581433","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T13:39:02Z","timestamp":1750340342000},"page":"4749-4761","source":"Crossref","is-referenced-by-count":0,"title":["DeepPartitioning: Deep Learning of Graph Partitioning for Neuron Segmentation From Electron Microscopy Volume via Graph Neural Network"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5015-3529","authenticated-orcid":false,"given":"Zhenchen","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0553-4581","authenticated-orcid":false,"given":"Xu","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2001-1597","authenticated-orcid":false,"given":"Jiazheng","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9313-5815","authenticated-orcid":false,"given":"Yanchao","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Bing","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2148-1846","authenticated-orcid":false,"given":"Zhiyong","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4713-4631","authenticated-orcid":false,"given":"Hua","family":"Han","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1126\/science.adk4858"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2022.01.023"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1126\/science.aay3134"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/nature22356"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2021.11.037"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.4151"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3409634"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2835450"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-022-01711-z"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3176050"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2712360"},{"key":"ref12","article-title":"Superhuman accuracy on the SNEMI3D connectomics challenge","author":"Lee","year":"2017","journal-title":"arXiv:1706.00120"},{"key":"ref13","first-page":"1966","article-title":"Combinatorial energy learning for image segmentation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Maitin-Shepard"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2009.10-08-881"},{"key":"ref15","article-title":"Image segmentation by size-dependent single linkage clustering of a watershed basin graph","author":"Zlateski","year":"2015","journal-title":"arXiv:1505.00249"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/34.87344"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-69321-5_15"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01135"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23094-3_3"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33712-3_56"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2303095"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.204"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/BF01581239"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1023\/B:MACH.0000033116.57574.95"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2006.05.008"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1038\/s43586-024-00294-7"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3233\/FI-2000-411207"},{"key":"ref29","first-page":"2843","article-title":"Deep neural networks segment neuronal membranes in electron microscopy images","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Ciresan"},{"key":"ref30","first-page":"1","article-title":"Learned versus hand-designed feature representations for 3D agglomeration","volume-title":"Proc. 2nd Int. Conf. Learn. Represent.","author":"Bogovic"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/158"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3097826"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2836300"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2505181"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2449552"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00971"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00219"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2980827"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1970.tb01770.x"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553473"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.softx.2019.100335"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3112586"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2024.3388373"},{"key":"ref45","first-page":"6351","article-title":"Learning combinatorial optimization algorithms over graphs","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Dai"},{"key":"ref46","first-page":"6412","article-title":"Graph convolutional policy network for goal-directed molecular graph generation","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"You"},{"key":"ref47","first-page":"13757","article-title":"GeoMol: Torsional geometric generation of molecular 3D conformer ensembles","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ganea"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-023-00503-5"},{"issue":"130","key":"ref49","first-page":"1","article-title":"Combinatorial optimization and reasoning with graph neural networks","volume":"24","author":"Cappart","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-021-00155-3"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/694"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3005590"},{"key":"ref53","first-page":"1","article-title":"Learning deep graph matching with channel-independent embedding and Hungarian attention","volume-title":"Proc. 8th Int. Conf. Learn. Represent.","author":"Yu"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.3389\/fnana.2015.00142"},{"key":"ref55","first-page":"1","article-title":"Return of the devil in the details: Delving deep into convolutional nets","volume-title":"Proc. Brit. Mach. Vis. Conf. (BMVC)","author":"Chatfifield"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/BF02854581"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2015.06.054"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1509820112"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2006.11.013"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-018-0049-4"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87193-2_17"},{"key":"ref63","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref64","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kipf"},{"key":"ref65","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Veli\u010dkovi\u0107"},{"key":"ref66","article-title":"A critical review of recurrent neural networks for sequence learning","author":"Lipton","year":"2015","journal-title":"arXiv:1506.00019"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/42\/11279972\/11044413.pdf?arnumber=11044413","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T18:40:54Z","timestamp":1765219254000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11044413\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":67,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2025.3581433","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"type":"print","value":"0278-0062"},{"type":"electronic","value":"1558-254X"}],"subject":[],"published":{"date-parts":[[2025,12]]}}}