{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T08:07:19Z","timestamp":1789978039849,"version":"4.0.1"},"reference-count":44,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":16,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["nyaspubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ann NY Acad Sci"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Accurate kidney tumor segmentation is critical for surgical planning but is challenged by indistinct boundaries and high morphological variability in computed tomography (CT) images. We propose the adaptive boundary\u2010aware network (MABS\u2010Net). The architecture integrates three core innovations: (1) a boundary\u2010aware multiscale feature extraction module using learnable boundary\u2010enhancing convolutions and adaptive weight maps to capture subtle edge cues; (2) an adaptive three\u2010stage cascaded strategy for progressive refinement from coarse localization to uncertainty\u2010driven boundary optimization; and (3) a contrastive learning mechanism with online hard example mining to explicitly boost feature discrimination between tumor and normal tissues in ambiguous regions. Experiments on the KiTS19 and KiTS21 datasets demonstrate MABS\u2010Net's superiority. On KiTS19, it achieved a Dice coefficient of 0.891 \u00b1 0.034, significantly outperforming the nnU\u2010Net baseline. Notably, the 95% Hausdorff distance (HD95) was reduced to 6.73 \u00b1 2.28\u00a0mm, and the boundary Dice score improved by 5.8% compared to state\u2010of\u2010the\u2010art methods, validating our boundary\u2010aware design. Furthermore, the model provides pixel\u2010wise uncertainty maps to support reliable clinical decision\u2010making. MABS\u2010Net balances high accuracy with computational efficiency (0.53 s\/case), presenting a promising solution for automated renal tumor analysis.<\/jats:p>","DOI":"10.1111\/nyas.70231","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T13:38:57Z","timestamp":1773754737000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Kidney Tumor Segmentation With a Multistage Adaptive Boundary\u2010Aware Network"],"prefix":"10.1111","volume":"1557","author":[{"given":"Ruoyu","family":"Wu","sequence":"first","affiliation":[{"name":"Shanghai Medical College, Fudan University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Shi","sequence":"additional","affiliation":[{"name":"Shanghai Medical College, Fudan University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jitao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Emergency Equipment College University of Emergency Management (formerly North China Institute of Science and Technology)  Sanhe China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12885\u2010025\u201015130\u20108"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_10_5_1","unstructured":"O.Oktay J.Schlemper L. L.Folgoc et\u00a0al.Attention U\u2010Net: Learning Where to Look for the Pancreas(2018)arXiv https:\/\/doi.org\/10.48550\/arXiv.1804.03999."},{"key":"e_1_2_10_6_1","doi-asserted-by":"crossref","unstructured":"Z.Zhou M. M. R.Siddiquee N.Tajbakhsh andJ.Liang \u201cUNet++: A Nested U\u2010Net Architecture for Medical Image Segmentation \u201d inDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop DLMIA 2018 and 8th International Workshop ML\u2010CDS 2018 Held in Conjunction With MICCAI 2018 Granada Spain S 11045(2018) 3\u201311 https:\/\/doi.org\/10.1007\/978\u20103\u2010030\u201000889\u20105_1.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"e_1_2_10_7_1","unstructured":"J.Chen Y.Lu Q.Yu et\u00a0al.TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation(2021)arXiv https:\/\/doi.org\/10.48550\/arXiv.2102.04306."},{"key":"e_1_2_10_8_1","unstructured":"N.Heller F.Isensee D.Trofimova et\u00a0al.The KiTS21 Challenge: Automatic Segmentation of Kidneys Renal Tumors and Renal Cysts in Corticomedullary\u2010Phase computed tomography (CT)(2023).arXiv https:\/\/doi.org\/10.48550\/arXiv.2307.01984."},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101821"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101851"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1986.4767851"},{"key":"e_1_2_10_13_1","volume-title":"Digital Image Processing","author":"Gonzalez R. C.","year":"2002"},{"key":"e_1_2_10_14_1","doi-asserted-by":"crossref","unstructured":"F.Chollet \u201cXception: Deep Learning With Depthwise Separable Convolutions \u201d in2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1800\u20131807 https:\/\/doi.org\/10.1109\/CVPR.2017.195.","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_2_10_15_1","unstructured":"A. G.Howard M.Zhu B.Chen et\u00a0al.MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications(2017).arXiv https:\/\/doi.org\/10.48550\/arXiv.1704.04861."},{"key":"e_1_2_10_16_1","doi-asserted-by":"crossref","unstructured":"S.Mehta M.Rastegari A.Caspi L.Shapiro andH.HajishirziESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation(2018).arXiv https:\/\/doi.org\/10.48550\/arXiv.1803.06815.","DOI":"10.1007\/978-3-030-01249-6_34"},{"key":"e_1_2_10_17_1","unstructured":"F.YuandV.KoltunMulti\u2010Scale Context Aggregation by Dilated Convolutions(2016).arXiv https:\/\/doi.org\/10.48550\/arXiv.1511.07122."},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/4792532"},{"key":"e_1_2_10_19_1","doi-asserted-by":"crossref","unstructured":"Y.George \u201cA Coarse\u2010to\u2010Fine 3D U\u2010Net Network for Semantic Segmentation of Kidney computed tomography (CT) Scans \u201d inMICCAI 2021 Challenge Kits 2021 Held in Conjunction With MICCAI 2021 Proceedings(2022) 137\u2013142 https:\/\/doi.org\/10.1007\/978\u20103\u2010030\u201098385\u20107_18.","DOI":"10.1007\/978-3-030-98385-7_18"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106861"},{"key":"e_1_2_10_21_1","doi-asserted-by":"crossref","unstructured":"G.Santini N.Moreau andM.Rubeaux Kidney Tumor Segmentation Using an Ensembling Multi\u2010Stage Deep Learning Approach. A Contribution to the KiTS19 Challenge(2020).arXiv https:\/\/doi.org\/10.48550\/arXiv.1909.00735.","DOI":"10.24926\/548719.023"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.3233\/THC\u2010232009"},{"key":"e_1_2_10_23_1","unstructured":"T.Chen S.Kornblith M.Norouzi andG.HintonA Simple Framework for Contrastive Learning of Visual Representations(2020).arXiv https:\/\/doi.org\/10.48550\/arXiv.2002.05709."},{"key":"e_1_2_10_24_1","doi-asserted-by":"crossref","unstructured":"M.Caron H.Touvron I.Misra et\u00a0al.Emerging Properties in Self\u2010Supervised Vision Transformers(2021).arXiv https:\/\/doi.org\/10.48550\/arXiv.2104.14294.","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"e_1_2_10_25_1","unstructured":"K.He X.Chen S.Xie Y.Li P.Doll\u00e1r andR.GirshickMasked Autoencoders Are Scalable Vision Learners(2021).arXiv https:\/\/doi.org\/10.48550\/arXiv.2111.06377."},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746\u2010023\u201000811\u20100"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592\u2010020\u201001008\u2010z"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3226268"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109728"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107086"},{"key":"e_1_2_10_31_1","doi-asserted-by":"crossref","unstructured":"S.Hu Y.Ye Z.Liao andY.XiaBoundary\u2010Aware Network for Kidney Parsing(2022).arXiv https:\/\/doi.org\/10.48550\/arXiv.2208.13338.","DOI":"10.1007\/978-3-031-27324-7_2"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2025.3556310"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2025.3599066"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2025.3601359"},{"key":"e_1_2_10_35_1","doi-asserted-by":"crossref","unstructured":"A.Hatamizadeh V.Nath Y.Tang D.Yang H.Roth andD.XuSwin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in magnetic resonance imaging (MRI) Images(2022).arXiv https:\/\/doi.org\/10.48550\/arXiv.2201.01266.","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"e_1_2_10_36_1","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2024.1398237"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3264513"},{"key":"e_1_2_10_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3130469"},{"key":"e_1_2_10_39_1","unstructured":"A.KendallandY.GalWhat Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? (2017).arXiv https:\/\/doi.org\/10.48550\/arXiv.1703.04977."},{"key":"e_1_2_10_40_1","first-page":"6964","article-title":"A Probabilistic U\u2010Net for Segmentation of Ambiguous Images","volume":"31","author":"Kohl S.","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_10_41_1","doi-asserted-by":"crossref","unstructured":"T.\u2010Y.Lin P.Goyal R.Girshick K.He andP.Doll\u00e1rFocal Loss for Dense Object Detection(2018).arXiv https:\/\/doi.org\/10.48550\/arXiv.1708.02002.","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_2_10_42_1","doi-asserted-by":"crossref","unstructured":"A.Shrivastava A.Gupta andR.Girshick \u201cTraining Region\u2010Based Object Detectors With Online Hard Example Mining \u201d in2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2016) 761\u2013769 https:\/\/doi.org\/10.1109\/CVPR.2016.89.","DOI":"10.1109\/CVPR.2016.89"},{"key":"e_1_2_10_43_1","doi-asserted-by":"publisher","DOI":"10.2196\/26151"},{"key":"e_1_2_10_44_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020192224"},{"key":"e_1_2_10_45_1","doi-asserted-by":"crossref","unstructured":"M.Havaei N.Guizard N.Chapados andY.BengioHeMIS: Hetero\u2010Modal Image Segmentation(2016). arXiv https:\/\/doi.org\/10.48550\/arXiv.1607.05194.","DOI":"10.1007\/978-3-319-46723-8_54"}],"container-title":["Annals of the New York Academy of Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/nyaspubs.onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/nyas.70231","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/nyaspubs.onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/nyas.70231","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/nyaspubs.onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/nyas.70231","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T07:54:01Z","timestamp":1789977241000},"score":1,"resource":{"primary":{"URL":"https:\/\/nyaspubs.onlinelibrary.wiley.com\/doi\/10.1111\/nyas.70231"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["10.1111\/nyas.70231"],"URL":"https:\/\/doi.org\/10.1111\/nyas.70231","archive":["Portico"],"relation":{},"ISSN":["0077-8923","1749-6632"],"issn-type":[{"value":"0077-8923","type":"print"},{"value":"1749-6632","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"2025-11-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70231"}}