{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T23:06:56Z","timestamp":1762643216916,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819543977"},{"type":"electronic","value":"9789819543984"}],"license":[{"start":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:00:00Z","timestamp":1762646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:00:00Z","timestamp":1762646400000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-4398-4_26","type":"book-chapter","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T16:36:59Z","timestamp":1762619819000},"page":"366-380","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DDU-Net: A Dempster-Shafer Theory-Aided Diffusion-Based U-Net Model for\u00a0Microscopic Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Saptarshi","family":"Pani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asya","family":"Lyanova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dmitrii","family":"Kaplun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ram","family":"Sarkar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,9]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybernet. (1979)","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"26_CR2","unstructured":"Serra, J.: Image analysis and mathematical morphology. Academic Press (1983)"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: Algorithms based on hamilton\u2013jacobi formulations. J. Computat. Phys. (1988)","DOI":"10.1016\/0021-9991(88)90002-2"},{"key":"26_CR4","unstructured":"Boykov, Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. In: ICCV (2001)"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: Convolutional networks for biomedical image segmentation. In: MICCAI, U-net (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"26_CR7","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS (2020)"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Saharia, C., Chan, W., Salimans, T., et\u00a0al.: Image super-resolution via iterative refinement. IEEE Trans. Pattern Analy. Mach. Intell. (2022)","DOI":"10.1109\/TPAMI.2022.3204461"},{"key":"26_CR9","unstructured":"Amit, T., Shaharbany, T., Nachmani, E., Wolf, L.: Segdiff: Image segmentation with diffusion probabilistic models. arXiv preprint arXiv:2112.00390(2021)"},{"key":"26_CR10","unstructured":"Wu, J., Fu, R., Fang, H., et\u00a0al.: Medsegdiff: medical image segmentation with diffusion probabilistic model. arXiv preprint arXiv:2301.12345 (2023)"},{"key":"26_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107140","volume":"100","author":"G Yue","year":"2025","unstructured":"Yue, G., Ma, X., Li, W., An, Z., Yang, C.: 2mspk-net: a nuclei segmentation network based on multi-scale, multi-dimensional attention, and sam prior knowledge. Biomed. Signal Process. Control 100, 107140 (2025)","journal-title":"Biomed. Signal Process. Control"},{"key":"26_CR12","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1016\/j.csbj.2023.12.042","volume":"23","author":"A Mahbod","year":"2024","unstructured":"Mahbod, A., Dorffner, G., Ellinger, I., Woitek, R., Hatamikia, S.: Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization. Comput. Struct. Biotechnol. J. 23, 669\u2013678 (2024)","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"26_CR13","doi-asserted-by":"publisher","unstructured":"Roy, A., Pramanik, P., Ghosal, S., Valenkova, D., Kaplun, D., Sarkar, R.: GRU-Net: gaussian attention aided dense skip connection based MultiResUNet for breast histopathology image segmentation. In: Yap, M.H., Kendrick, C., Behera, A., Cootes, T., Zwiggelaar, R. (eds.) Medical Image Understanding and Analysis. MIUA 2024. LNCS, vol. 14859 (2024). Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-031-66955-2_21","DOI":"10.1007\/978-3-031-66955-2_21"},{"key":"26_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112217","volume":"301","author":"J-H Chang","year":"2024","unstructured":"Chang, J.-H., Pei-Hsuan, W., Wang, T.-H., Chung, P.-C.: A-reseunet: achieve no-label binary segmentation of nuclei in histology images. Knowl.-Based Syst. 301, 112217 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"26_CR15","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.neunet.2023.11.034","volume":"170","author":"N Zhang","year":"2024","unstructured":"Zhang, N., et al.: Ct-net: asymmetric compound branch transformer for medical image segmentation. Neural Netw. 170, 298\u2013311 (2024)","journal-title":"Neural Netw."},{"key":"26_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106439","volume":"95","author":"L Li","year":"2024","unstructured":"Li, L., He, K., Zhu, X., Gou, F., Jia, W.: A pathology image segmentation framework based on deblurring and region proxy in medical decision-making system. Biomed. Signal Process. Control 95, 106439 (2024)","journal-title":"Biomed. Signal Process. Control"},{"issue":"19","key":"26_CR17","doi-asserted-by":"publisher","first-page":"57449","DOI":"10.1007\/s11042-023-17768-7","volume":"83","author":"A Kanadath","year":"2024","unstructured":"Kanadath, A., Jothi, J.A.A., Urolagin, S.: Air-unet++: a deep learning framework for histopathology image segmentation and detection. Multimedia Tools Appli. 83(19), 57449\u201357475 (2024)","journal-title":"Multimedia Tools Appli."},{"key":"26_CR18","doi-asserted-by":"publisher","unstructured":"Yu, X. et al.: Diffusion-based data augmentation for nuclei image segmentation. In: Greenspan, H., et al. (ed.) MICCAI 2023. LNCS, vol. 14227. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43993-3_57","DOI":"10.1007\/978-3-031-43993-3_57"},{"issue":"4","key":"26_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/ima.23111","volume":"34","author":"M Gour","year":"2024","unstructured":"Gour, M., Jain, S., Kumar, T.S.: Robust nuclei segmentation with encoder-decoder network from the histopathological images. Inter. J. Imaging Syst. Technol. 34(4), e23111 (2024)","journal-title":"Inter. J. Imaging Syst. Technol."},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Im, Y.-H., Park, S.-H., Lee, S.-C.: Hda-net: H &e and rgb dual attention network for nuclei instance segmentation. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3390726"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhang, D., Lin, Y., Feng, Y., Tang, J.:Merging context clustering with visual state space models for medical image segmentation. IEEE Trans. Med. Imaging (2025)","DOI":"10.32388\/NUPPZA"},{"key":"26_CR22","doi-asserted-by":"publisher","unstructured":"Shui, Z. et al.: Unleashing the power of prompt-driven nucleus instance segmentation. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) Computer Vision \u2013 ECCV 2024. ECCV 2024. LNCs, vol. 15085. Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-031-73383-3_17","DOI":"10.1007\/978-3-031-73383-3_17"},{"issue":"2","key":"26_CR23","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1214\/aoms\/1177698950","volume":"38","author":"AP Dempster","year":"1967","unstructured":"Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38(2), 325\u2013339 (1967)","journal-title":"Ann. Math. Stat."},{"key":"26_CR24","unstructured":"Shaharabany, T., Wolf, L.: Zero-shot medical image segmentation based on sparse prompt using finetuned sam. Med. Imaging Deep Learn. (2024)"},{"issue":"1","key":"26_CR25","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1038\/s41597-022-01681-z","volume":"9","author":"Q Chen","year":"2022","unstructured":"Chen, Q., et al.: A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers. Sci. Data 9(1), 587 (2022)","journal-title":"Sci. Data"},{"issue":"7","key":"26_CR26","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/TMI.2017.2677499","volume":"36","author":"N Kumar","year":"2017","unstructured":"Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., Sethi, A.: A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans. Med. Imaging 36(7), 1550\u20131560 (2017)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4398-4_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T23:02:21Z","timestamp":1762642941000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4398-4_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,9]]},"ISBN":["9789819543977","9789819543984"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4398-4_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,11,9]]},"assertion":[{"value":"9 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gold Coast, QLD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.acpr2025.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}