{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T05:49:22Z","timestamp":1792043362683,"version":"4.4.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:00:00Z","timestamp":1791936000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:00:00Z","timestamp":1791936000000},"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":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-02390-4","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T05:37:26Z","timestamp":1792042646000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CAS-UNet: A Context-Adaptive Encoder-Decoder Network with Boundary-Distance Auxiliary Supervision for Histopathological Nuclei Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5382-0542","authenticated-orcid":false,"given":"Wei","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,14]]},"reference":[{"key":"2390_CR1","doi-asserted-by":"crossref","unstructured":"Verghese, G., Lennerz, J.K., Ruta, D., et al.: Computational pathology in cancer diagnosis, prognosis, and prediction\u2014present day and prospects. The Journal of Pathology 260(5), 551\u2013563 (2023)","DOI":"10.1002\/path.6163"},{"key":"2390_CR2","doi-asserted-by":"crossref","unstructured":"Baxi, V., Edwards, R., Montalto, M., Saha, S.: Digital pathology and artificial intelligence in translational medicine and clinical practice. Modern Pathology 35(1), 23\u201332 (2022)","DOI":"10.1038\/s41379-021-00919-2"},{"key":"2390_CR3","doi-asserted-by":"crossref","unstructured":"Campanella, G., Hanna, M.G., Geneslaw, L., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine 25(8), 1301\u20131309 (2019)","DOI":"10.1038\/s41591-019-0508-1"},{"key":"2390_CR4","doi-asserted-by":"crossref","unstructured":"Naylor, P., La\u00e9, M., Reyal, F., et al.: Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Transactions on Medical Imaging 38(2), 448\u2013459 (2018)","DOI":"10.1109\/TMI.2018.2865709"},{"key":"2390_CR5","doi-asserted-by":"crossref","unstructured":"Jung, H., Lodhi, B., Kang, J.: An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images. BMC Biomedical Engineering 1(1), 24 (2019)","DOI":"10.1186\/s42490-019-0026-8"},{"key":"2390_CR6","doi-asserted-by":"publisher","unstructured":"Wu, Y., He, K.: Group normalization. In: Proceedings of the European Conference on Computer Vision, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_1","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"2390_CR7","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241. Springer, Cham (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2390_CR8","doi-asserted-by":"publisher","unstructured":"Deng, R., Hu, S., Huang, P., Sun, Y., et al.: TexSem-Net: Texture augmentation and regional semantics fusion dense-prediction network for cervical squamous cell carcinoma histopathological images. IEEE Transactions on Circuits and Systems for Video Technology (2026) https:\/\/doi.org\/10.1109\/TCSVT.2026.3690517","DOI":"10.1109\/TCSVT.2026.3690517"},{"key":"2390_CR9","doi-asserted-by":"publisher","unstructured":"Meng, F., et al.: DEW-Net: A w-shaped dual-encoder network with attention fusion mechanisms for pathological h&e image segmentation. IEEE Journal of Biomedical and Health Informatics (2026) https:\/\/doi.org\/10.1109\/JBHI.2026.3658523","DOI":"10.1109\/JBHI.2026.3658523"},{"key":"2390_CR10","doi-asserted-by":"publisher","unstructured":"Pang, L., He, P., Han, Y., Cui, H., Feng, P., Zhang, C., Huang, P., Tian, S.: Semantic consistency network with edge learner and connectivity enhancer for cervical tumor segmentation from histopathology images. Journal of Medical Systems 49 (2025) https:\/\/doi.org\/10.1007\/s10916-025-02123-4","DOI":"10.1007\/s10916-025-02123-4"},{"key":"2390_CR11","doi-asserted-by":"publisher","unstructured":"Deng, R., et al.: MCRANet: Mtsl-based connectivity region attention network for pd-l1 status segmentation in h&e stained images. Computers in Biology and Medicine 181, 109357 (2024) https:\/\/doi.org\/10.1016\/j.compbiomed.2024.109357","DOI":"10.1016\/j.compbiomed.2024.109357"},{"key":"2390_CR12","doi-asserted-by":"crossref","unstructured":"Wang, Q., Deng, X., Huang, P., et al.: Prediction of PD-L1 tumor positive score in lung squamous cell carcinoma with H&E staining images and deep learning. Frontiers in Artificial Intelligence 7, 1452563 (2024)","DOI":"10.3389\/frai.2024.1452563"},{"key":"2390_CR13","doi-asserted-by":"publisher","unstructured":"Ma, X., et al.: A multi-instance learning network with prototype-instance adversarial contrastive for cervix pathology grading. Medical Image Analysis (2026) https:\/\/doi.org\/10.1016\/j.media.2025.103880","DOI":"10.1016\/j.media.2025.103880"},{"key":"2390_CR14","doi-asserted-by":"crossref","unstructured":"Mao, Y., Xu, X., Luo, J., et al.: Mani-NENet: Manifold neuron energy network-enhancing dysplastic cells perception for lung adenocarcinoma subtypes recognition. IEEE Transactions on Instrumentation and Measurement (2026)","DOI":"10.1109\/TIM.2026.3670553"},{"key":"2390_CR15","doi-asserted-by":"crossref","unstructured":"Li, C., Bozorgtabar, B., Ping, Y., et al.: Positive semi-definite latent factor grouping-boosted cluster-reasoning instance disentangled learning for WSI representation. arXiv preprint arXiv:2511.01304 (2025)","DOI":"10.1007\/978-3-032-38224-5_38"},{"key":"2390_CR16","doi-asserted-by":"publisher","unstructured":"Li, C., Huang, P.: Prototype instance-semantic disentanglement with low-rank regularized subspace clustering for WSIs explainable recognition. arXiv preprint arXiv:2602.14501 (2026) https:\/\/doi.org\/10.48550\/arXiv.2602.14501","DOI":"10.48550\/arXiv.2602.14501"},{"key":"2390_CR17","doi-asserted-by":"crossref","unstructured":"Huang, P., He, P., Tian, S., et al.: A ViT-AMC network with adaptive model fusion and multiobjective optimization for interpretable laryngeal tumor grading from histopathological images. IEEE Transactions on Medical Imaging 42(1), 15\u201328 (2023)","DOI":"10.1109\/TMI.2022.3202248"},{"key":"2390_CR18","doi-asserted-by":"publisher","unstructured":"Huang, P., Li, C., He, P., Xiao, H., Ping, Y., Feng, P., Tian, S., Chen, H., Mercaldo, F., Santone, A., Yeh, H.-y., Qin, J.: MamlFormer: Priori-experience guiding transformer network via manifold adversarial multi-modal learning for laryngeal histopathological grading. Information Fusion (2024) https:\/\/doi.org\/10.1016\/j.inffus.2024.102333","DOI":"10.1016\/j.inffus.2024.102333"},{"key":"2390_CR19","doi-asserted-by":"crossref","unstructured":"Graham, S., Vu, Q.D., Jahanifar, M., et al.: CoNIC challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting. Medical Image Analysis 92, 103047 (2024)","DOI":"10.1016\/j.media.2023.103047"},{"key":"2390_CR20","doi-asserted-by":"crossref","unstructured":"Vu, Q.D., Graham, S., Kurc, T., et al.: Methods for segmentation and classification of digital microscopy tissue images. Frontiers in Bioengineering and Biotechnology 7, 53 (2019)","DOI":"10.3389\/fbioe.2019.00053"},{"key":"2390_CR21","doi-asserted-by":"crossref","unstructured":"Kumar, N., Verma, R., Anand, D., et al.: A multi-organ nucleus segmentation challenge. IEEE Transactions on Medical Imaging 39(5), 1380\u20131391 (2019)","DOI":"10.1109\/TMI.2019.2947628"},{"key":"2390_CR22","doi-asserted-by":"crossref","unstructured":"Mahbod, A., Polak, C., Feldmann, K., et al.: NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images. Scientific Data 11, 295 (2024)","DOI":"10.1038\/s41597-024-03117-2"},{"key":"2390_CR23","unstructured":"Gamper, J., Koohbanani, N.A., Benes, K., et al.: PanNuke dataset extension, insights and baselines. arXiv preprint arXiv:2003.10778 (2020)"},{"key":"2390_CR24","doi-asserted-by":"crossref","unstructured":"Jha, D., Riegler, M.A., Johansen, D., et al.: DoubleU-Net: A deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, pp. 558\u2013564. IEEE, Piscataway, NJ (2020)","DOI":"10.1109\/CBMS49503.2020.00111"},{"key":"2390_CR25","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"2390_CR26","doi-asserted-by":"crossref","unstructured":"He, Z., Unberath, M., Ke, J., et al.: TransNuSeg: A lightweight multi-task transformer for nuclei segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 206\u2013215. Springer, Cham (2023)","DOI":"10.1007\/978-3-031-43901-8_20"},{"key":"2390_CR27","doi-asserted-by":"crossref","unstructured":"Roy, A., Pramanik, P., Kaplun, D., et al.: AWGUNet: Attention-aided wavelet guided U-Net for nuclei segmentation in histopathology images. In: IEEE International Symposium on Biomedical Imaging, pp. 1\u20134 (2024)","DOI":"10.1109\/ISBI56570.2024.10635449"},{"key":"2390_CR28","doi-asserted-by":"crossref","unstructured":"Huang, X., Ma, Y., Mei, X., et al.: Lesion boundary detection for skin lesion segmentation based on boundary sensing and CNN-transformer fusion networks. Artificial Intelligence in Medicine 167, 103190 (2025)","DOI":"10.1016\/j.artmed.2025.103190"},{"key":"2390_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, S., Li, Y., Lu, L., et al.: MLAANet: A multi-level attention-aware network for pathological nuclei segmentation. Biomedical Signal Processing and Control 112, 108716 (2026)","DOI":"10.1016\/j.bspc.2025.108716"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02390-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02390-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02390-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T05:37:30Z","timestamp":1792042650000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02390-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,14]]},"references-count":29,"alternative-id":["2390"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02390-4","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,14]]},"assertion":[{"value":"30 July 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study only uses publicly available open datasets, and no new human tissue samples were collected. Ethical approval is not required.","order":1,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study is based on publicly available anonymized histopathological datasets. No human participants were directly recruited or involved in the research process, so additional informed consent for participation is not applicable.","order":2,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All data and images used in this study are from publicly available anonymized datasets with formal publication consent. No identifiable personal information of patients is included in this manuscript.","order":3,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","label":"Competing Interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}