{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T02:56:55Z","timestamp":1740106615999,"version":"3.37.3"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100010726","name":"Northwest Normal University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010726","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\/501100009620","name":"gansu sheng kexue jishu ting","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009620","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Visual Communication and Image Representation"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1016\/j.jvcir.2024.104115","type":"journal-article","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T16:36:22Z","timestamp":1710174982000},"page":"104115","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Dense-sparse representation matters: A point-based method for volumetric medical image segmentation"],"prefix":"10.1016","volume":"100","author":[{"given":"Yun","family":"Jiang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7976-5043","authenticated-orcid":false,"given":"Bingxi","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5566-761X","authenticated-orcid":false,"given":"Zequn","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9993-9286","authenticated-orcid":false,"given":"Yao","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Huanting","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Yuhang","family":"Li","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.jvcir.2024.104115_b1","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"year":"2017","series-title":"Attention is all you need","author":"Vaswani","key":"10.1016\/j.jvcir.2024.104115_b2"},{"key":"10.1016\/j.jvcir.2024.104115_b3","series-title":"Milletari2016","first-page":"424","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"10.1016\/j.jvcir.2024.104115_b4","series-title":"2016 Fourth International Conference on 3D Vision","first-page":"565","author":"Milletari","year":"2016"},{"key":"10.1016\/j.jvcir.2024.104115_b5","series-title":"Unet++: a nested U-net architecture for medical image segmentation","first-page":"3","author":"Zhou","year":"2018"},{"year":"2022","series-title":"Nnformer: interleaved transformer for volumetric segmentation","author":"Zhou","key":"10.1016\/j.jvcir.2024.104115_b6"},{"key":"10.1016\/j.jvcir.2024.104115_b7","series-title":"U-net: convolutional networks for biomedical image segmentation","first-page":"234","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.jvcir.2024.104115_b8","unstructured":"Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y. Hammerla, Bernhard Kainz, Ben Glocker, Daniel Rueckert, Attention U-Net: Learning Where to Look for the Pancreas."},{"key":"10.1016\/j.jvcir.2024.104115_b9","series-title":"Fully convolutional networks for semantic segmentation","first-page":"3431","author":"Long","year":"2015"},{"key":"10.1016\/j.jvcir.2024.104115_b10","series-title":"Nnu-net for brain tumor segmentation","first-page":"118","author":"Isensee","year":"2020"},{"issue":"2","key":"10.1016\/j.jvcir.2024.104115_b11","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"Isensee","year":"2021","journal-title":"Nature Methods"},{"key":"10.1016\/j.jvcir.2024.104115_b12","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","first-page":"234","author":"Ronneberger","year":"2015"},{"year":"2023","series-title":"Image as set of points","author":"Ma","key":"10.1016\/j.jvcir.2024.104115_b13"},{"year":"2021","series-title":"Chen2022a","author":"Yu","key":"10.1016\/j.jvcir.2024.104115_b14"},{"key":"10.1016\/j.jvcir.2024.104115_b15","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.isprsjprs.2022.09.017","volume":"194","author":"Chen","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.jvcir.2024.104115_b16","unstructured":"Yinyu Nie, Ji Hou, Xiaoguang Han, Matthias Niessner, Jiang2020a, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 4608\u20134618."},{"key":"10.1016\/j.jvcir.2024.104115_b17","volume":"89","author":"Li","year":"2022","journal-title":"J. Vis. Commun. Image Represent."},{"key":"10.1016\/j.jvcir.2024.104115_b18","series-title":"Stratified transformer for 3D point cloud segmentation","first-page":"8500","author":"Lai","year":"2022"},{"year":"2023","series-title":"Small but mighty: enhancing 3D point clouds semantic segmentation with u-next framework","author":"Zeng","key":"10.1016\/j.jvcir.2024.104115_b19"},{"year":"2023","series-title":"SAT: size-aware transformer for 3D point cloud semantic segmentation","author":"Zhou","key":"10.1016\/j.jvcir.2024.104115_b20"},{"key":"10.1016\/j.jvcir.2024.104115_b21","series-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"11105","author":"Hu","year":"2020"},{"issue":"10","key":"10.1016\/j.jvcir.2024.104115_b22","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"Menze","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.jvcir.2024.104115_b23","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"Bakas","year":"2017","journal-title":"Sci. Data"},{"year":"2019","series-title":"Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge","author":"Bakas","key":"10.1016\/j.jvcir.2024.104115_b24"},{"key":"10.1016\/j.jvcir.2024.104115_b25","series-title":"BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability","first-page":"102889","author":"Gao","year":"2023"},{"issue":"12","key":"10.1016\/j.jvcir.2024.104115_b26","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.1109\/TPAMI.2018.2869576","volume":"41","author":"Zhuang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"10.1016\/j.jvcir.2024.104115_b27","first-page":"6021","volume":"45","author":"Wu","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.jvcir.2024.104115_b28","series-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","first-page":"231","author":"Jiang","year":"2020"},{"key":"10.1016\/j.jvcir.2024.104115_b29","volume":"150","author":"Zhang","year":"2022","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"10.1016\/j.jvcir.2024.104115_b30","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1109\/TMI.2018.2878669","volume":"38","author":"Dolz","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"year":"2017","series-title":"3D densely convolutional networks for volumetric segmentation","author":"Bui","key":"10.1016\/j.jvcir.2024.104115_b31"},{"key":"10.1016\/j.jvcir.2024.104115_b32","series-title":"Automatic 3D cardiovascular MR segmentation with densely-connected volumetric ConvNets","first-page":"287","author":"Yu","year":"2017"},{"key":"10.1016\/j.jvcir.2024.104115_b33","series-title":"SegNetr: rethinking the local-global interactions and skip connections in u-shaped networks","first-page":"64","author":"Cheng","year":"2023"},{"year":"2021","series-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","key":"10.1016\/j.jvcir.2024.104115_b34"},{"year":"2021","series-title":"Transbts: multimodal brain tumor segmentation using transformer","author":"Wang","key":"10.1016\/j.jvcir.2024.104115_b35"},{"key":"10.1016\/j.jvcir.2024.104115_b36","doi-asserted-by":"crossref","unstructured":"Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger R. Roth, Daguang Xu, UNETR: Transformers for 3D Medical Image Segmentation, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2022, pp. 574\u2013584.","DOI":"10.1109\/WACV51458.2022.00181"},{"year":"2022","series-title":"UNetFormer: a unified vision transformer model and pre-training framework for 3D medical image segmentation","author":"Hatamizadeh","key":"10.1016\/j.jvcir.2024.104115_b37"},{"year":"2023","series-title":"U-netmer: u-net meets transformer for medical image segmentation","author":"He","key":"10.1016\/j.jvcir.2024.104115_b38"},{"issue":"6","key":"10.1016\/j.jvcir.2024.104115_b39","doi-asserted-by":"crossref","first-page":"797","DOI":"10.3390\/brainsci12060797","volume":"12","author":"Jiang","year":"2022","journal-title":"Brain Sci."},{"year":"2021","series-title":"TransUNet: transformers make strong encoders for medical image segmentation","author":"Chen","key":"10.1016\/j.jvcir.2024.104115_b40"},{"key":"10.1016\/j.jvcir.2024.104115_b41","series-title":"Swin-unet: unet-like pure transformer for medical image segmentation","first-page":"205","author":"Cao","year":"2021"},{"key":"10.1016\/j.jvcir.2024.104115_b42","series-title":"PointNet: deep learning on point sets for 3D classification and segmentation","first-page":"652","author":"Qi","year":"2017"},{"volume":"30","year":"2017","author":"Qi","key":"10.1016\/j.jvcir.2024.104115_b43"},{"volume":"31","year":"2018","author":"Li","key":"10.1016\/j.jvcir.2024.104115_b44"},{"key":"10.1016\/j.jvcir.2024.104115_b45","series-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"5588","author":"Yan","year":"2020"},{"key":"10.1016\/j.jvcir.2024.104115_b46","volume":"79","author":"Lee","year":"2021","journal-title":"J. Vis. Commun. Image Represent."},{"year":"2022","series-title":"Rethinking network design and local geometry in point cloud: a simple residual MLP framework","author":"Ma","key":"10.1016\/j.jvcir.2024.104115_b47"},{"key":"10.1016\/j.jvcir.2024.104115_b48","series-title":"Point-unet: a context-aware point-based neural network for volumetric segmentation","first-page":"644","author":"Ho","year":"2022"},{"key":"10.1016\/j.jvcir.2024.104115_b49","series-title":"Segment anything","first-page":"4015","author":"Kirillov","year":"2023"}],"updated-by":[{"updated":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"DOI":"10.1016\/j.jvcir.2024.104205","type":"erratum","source":"publisher","label":"Erratum"}],"container-title":["Journal of Visual Communication and Image Representation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1047320324000701?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1047320324000701?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T09:43:57Z","timestamp":1717407837000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1047320324000701"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":49,"alternative-id":["S1047320324000701"],"URL":"https:\/\/doi.org\/10.1016\/j.jvcir.2024.104115","relation":{},"ISSN":["1047-3203"],"issn-type":[{"type":"print","value":"1047-3203"}],"subject":[],"published":{"date-parts":[[2024,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Dense-sparse representation matters: A point-based method for volumetric medical image segmentation","name":"articletitle","label":"Article Title"},{"value":"Journal of Visual Communication and Image Representation","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jvcir.2024.104115","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104115"}}