{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:36:56Z","timestamp":1779381416170,"version":"3.53.1"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031340475","type":"print"},{"value":"9783031340482","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-34048-2_56","type":"book-chapter","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T12:03:35Z","timestamp":1686139415000},"page":"730-742","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Rethinking Boundary Detection in\u00a0Deep Learning Models for\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Yi","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kwang-Ting","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"key":"56_CR1","doi-asserted-by":"crossref","unstructured":"Ahn, E., et al.: Automated saliency-based lesion segmentation in dermoscopic images. In: International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2015)","DOI":"10.1109\/EMBC.2015.7319025"},{"key":"56_CR2","doi-asserted-by":"crossref","unstructured":"Bi, L., Kim, J., Ahn, E., Feng, D., Fulham, M.: Automated skin lesion segmentation via image-wise supervised learning and multi-scale superpixel based cellular automata. In: IEEE International Symposium on Biomedical Imaging (ISBI) (2016)","DOI":"10.1109\/ISBI.2016.7493448"},{"issue":"9","key":"56_CR3","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.1109\/TBME.2017.2712771","volume":"64","author":"L Bi","year":"2017","unstructured":"Bi, L., Kim, J., Ahn, E., Kumar, A., Fulham, M., Feng, D.: Dermoscopic image segmentation via multistage fully convolutional networks. IEEE Trans. Biomed. Eng. 64(9), 2065\u20132074 (2017)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"56_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102680","volume":"84","author":"P Bilic","year":"2023","unstructured":"Bilic, P., et al.: The liver tumor segmentation benchmark (LiTS). Med. Image Anal. 84, 102680 (2023)","journal-title":"Med. Image Anal."},{"key":"56_CR5","unstructured":"Cao, H., et al.: Swin-UNet: UNet-like pure transformer for medical image segmentation. arXiv (2021)"},{"key":"56_CR6","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.neuroimage.2017.04.041","volume":"170","author":"H Chen","year":"2018","unstructured":"Chen, H., Dou, Q., Yu, L., Qin, J., Heng, P.A.: VoxresNet: deep voxelwise residual networks for brain segmentation from 3D MR images. Neuroimage 170, 446\u2013455 (2018)","journal-title":"Neuroimage"},{"key":"56_CR7","doi-asserted-by":"crossref","unstructured":"Chen, H., Qi, X., Yu, L., Heng, P.A.: DCAN: deep contour-aware networks for accurate gland segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.273"},{"key":"56_CR8","unstructured":"Chen, J., et al.: TransUnet: transformers make strong encoders for medical image segmentation. arXiv (2021)"},{"key":"56_CR9","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv (2017)"},{"key":"56_CR10","doi-asserted-by":"crossref","unstructured":"Chen, X., Dong, C., Ji, J., Cao, J., Li, X.: Image manipulation detection by multi-view multi-scale supervision. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01392"},{"key":"56_CR11","unstructured":"Codella, N., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC). arXiv (2019)"},{"key":"56_CR12","unstructured":"Dosovitskiy, A., et al.: An image is worth $$16\\times 16$$ words: transformers for image recognition at scale. In: International Conference on Learning Representations (ICLR) (2020)"},{"key":"56_CR13","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Ji, G.P., Sun, G., Cheng, M.M., Shen, J., Shao, L.: Camouflaged object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"56_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1007\/978-3-030-59710-8_64","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"S Fu","year":"2020","unstructured":"Fu, S., et al.: Domain adaptive relational reasoning for 3D multi-organ segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 656\u2013666. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_64"},{"issue":"2","key":"56_CR15","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"SH Gao","year":"2019","unstructured":"Gao, S.H., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., Torr, P.: Res2Net: a new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 652\u2013662 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"56_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/978-3-030-78191-0_41","volume-title":"Information Processing in Medical Imaging","author":"X Gao","year":"2021","unstructured":"Gao, X., Jin, Y., Zhao, Z., Dou, Q., Heng, P.-A.: Future frame prediction for robot-assisted surgery. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 533\u2013544. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_41"},{"key":"56_CR17","unstructured":"Graham, S., et al.: CoNIC: colon nuclei identification and counting challenge 2022. arXiv (2021)"},{"issue":"10","key":"56_CR18","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu, Z., et al.: CE-NET: context encoder network for 2D medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281\u20132292 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"56_CR19","unstructured":"Gutman, D., et al.: Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). arXiv (2016)"},{"key":"56_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1007\/978-3-031-08999-2_22","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"A Hatamizadeh","year":"2022","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. In: Crimi, A., Bakas, S. (eds.) BrainLes 2021. LNCS, vol. 12962, pp. 272\u2013284. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22"},{"key":"56_CR21","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: UNETR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV) (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"56_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"56_CR23","unstructured":"Huang, X., Deng, Z., Li, D., Yuan, X.: MISSFormer: an effective medical image segmentation transformer. arXiv (2021)"},{"key":"56_CR24","doi-asserted-by":"crossref","unstructured":"Irshad, S., Gomes, D.P., Kim, S.T.: Improved abdominal multi-organ segmentation via 3D boundary-constrained deep neural networks. arXiv (2022)","DOI":"10.1109\/ACCESS.2023.3264582"},{"issue":"2","key":"56_CR25","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1109\/4.996","volume":"23","author":"N Kanopoulos","year":"1988","unstructured":"Kanopoulos, N., Vasanthavada, N., Baker, R.L.: Design of an image edge detection filter using the Sobel operator. IEEE J. Solid-State Circ. 23(2), 358\u2013367 (1988)","journal-title":"IEEE J. Solid-State Circ."},{"key":"56_CR26","doi-asserted-by":"crossref","unstructured":"Lee, H.J., Kim, J.U., Lee, S., Kim, H.G., Ro, Y.M.: Structure boundary preserving segmentation for medical image with ambiguous boundary. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00487"},{"key":"56_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1007\/978-3-030-87240-3_47","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Lin","year":"2021","unstructured":"Lin, Y., Liu, L., Ma, K., Zheng, Y.: Seg4Reg+: consistency learning between spine segmentation and cobb angle regression. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 490\u2013499. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_47"},{"key":"56_CR28","unstructured":"Lin, Y., et al: Label propagation for annotation-efficient nuclei segmentation from pathology images. arXiv preprint arXiv:2202.08195 (2022)"},{"key":"56_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/978-3-030-32251-9_31","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Y Lin","year":"2019","unstructured":"Lin, Y., et al.: Automated pulmonary embolism detection from CTPA images using an end-to-end convolutional neural network. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 280\u2013288. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_31"},{"key":"56_CR30","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"56_CR31","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a, T., Ferreira, P.M., Marques, J.S., Marcal, A.R., Rozeira, J.: PH2-a dermoscopic image database for research and benchmarking. In: EMBC (2013)","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"56_CR32","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 3DV. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"56_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"56_CR34","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper, J., et al.: Attention gated networks: learning to leverage salient regions in medical images. Med. Image Anal. 53, 197\u2013207 (2019)","journal-title":"Med. Image Anal."},{"key":"56_CR35","doi-asserted-by":"crossref","unstructured":"Shamshad, F., et al.: Transformers in medical imaging: a survey. arXiv (2022)","DOI":"10.1016\/j.media.2023.102802"},{"key":"56_CR36","doi-asserted-by":"crossref","unstructured":"Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: gated axial-attention for medical image segmentation. arXiv (2021)","DOI":"10.1007\/978-3-030-87193-2_4"},{"key":"56_CR37","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (NeurIPS) (2017)"},{"key":"56_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1007\/978-3-030-78191-0_45","volume-title":"Information Processing in Medical Imaging","author":"PA Wijeratne","year":"2021","unstructured":"Wijeratne, P.A., Alexander, D.C., for the Alzheimer\u2019s Disease Neuroimaging Initiative: Learning transition times in event sequences: the temporal event-based model of disease progression. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 583\u2013595. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_45"},{"key":"56_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/978-3-031-16434-7_65","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"J Wu","year":"2022","unstructured":"Wu, J., et al.: SeATrans: learning segmentation-assisted diagnosis model via transformer. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13432, pp. 677\u2013687. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16434-7_65"},{"key":"56_CR40","unstructured":"Zhang, D., et al.: Deep learning for medical image segmentation: tricks, challenges and future directions. arXiv (2022)"},{"key":"56_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, D., Tang, J., Cheng, K.T.: Graph reasoning transformer for image parsing. In: ACM MM (2022)","DOI":"10.1145\/3503161.3547858"},{"key":"56_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS 2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34048-2_56","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T12:10:23Z","timestamp":1686139823000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34048-2_56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031340475","9783031340482"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34048-2_56","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"San Carlos de Bariloche","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Argentina","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"169","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"63","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}