{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T09:12:39Z","timestamp":1774602759176,"version":"3.50.1"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T00:00:00Z","timestamp":1771718400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T00:00:00Z","timestamp":1771718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Science and Technology Innovation Key R&D Program of Chongqing","award":["CSTB2024TIAD-STX0023"],"award-info":[{"award-number":["CSTB2024TIAD-STX0023"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276120"],"award-info":[{"award-number":["62276120"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"New Chongqing Youth Innovation Talent Program, China","award":["CSTB2024NSCQ-QCXMX0067"],"award-info":[{"award-number":["CSTB2024NSCQ-QCXMX0067"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s11263-026-02776-5","type":"journal-article","created":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T14:42:01Z","timestamp":1771771321000},"update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Physical Regularization Loss: Integrating Physical Knowledge to Image Segmentation"],"prefix":"10.1007","volume":"134","author":[{"given":"Yan","family":"Ding","sequence":"first","affiliation":[]},{"given":"Shuang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Huafeng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Guanqiu","family":"Qi","sequence":"additional","affiliation":[]},{"given":"Baisen","family":"Cong","sequence":"additional","affiliation":[]},{"given":"Yunpeng","family":"Gong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-3883-2529","authenticated-orcid":false,"given":"Zhiqin","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,22]]},"reference":[{"key":"2776_CR1","unstructured":"Anwesh, K., Pal, D., Ganguly, D., Chatterjee, K., & Roy, S. (2022). Number plate recognition from enhanced super-resolution using generative adversarial network. Multimed. Tools Appl., 1\u201317"},{"key":"2776_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102608","volume":"114","author":"KK Brar","year":"2025","unstructured":"Brar, K. K., Goyal, B., Dogra, A., Mustafa, M. A., Majumdar, R., Alkhayyat, A., & Kukreja, V. (2025). Image segmentation review: Theoretical background and recent advances. Information Fusion, 114, Article 102608. https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1016\/j.inffus.2024.102608","journal-title":"Information Fusion"},{"issue":"1","key":"2776_CR3","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1137\/0729012","volume":"29","author":"F Catt\u00e9","year":"1992","unstructured":"Catt\u00e9, F., Lions, P.-L., Morel, J.-M., & Coll, T. (1992). Image selective smoothing and edge detection by nonlinear diffusion. SIAM Journal on Numerical analysis, 29(1), 182\u2013193.","journal-title":"SIAM Journal on Numerical analysis"},{"key":"2776_CR4","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"11","key":"2776_CR5","doi-asserted-by":"publisher","first-page":"13023","DOI":"10.1109\/TITS.2022.3232153","volume":"24","author":"C Chen","year":"2023","unstructured":"Chen, C., Wang, C., Liu, B., He, C., Cong, L., & Wan, S. (2023). Edge intelligence empowered vehicle detection and image segmentation for autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 24(11), 13023\u201313034. https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1109\/TITS.2022.3232153","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"2776_CR6","doi-asserted-by":"crossref","unstructured":"Choi, H., Han, Y., Kim, D., Ham, S., Kim, M., Park, Y., & Hong, B.-W. (2020). Anisotropic diffusion with deep learning. In: International Conference on Machine Learning and Intelligent Systems. https:\/\/linproxy.fan.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:229375280","DOI":"10.3233\/FAIA200764"},{"key":"2776_CR7","unstructured":"Codella, N., Rotemberg, V., Tschandl, P., Celebi, M.E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., et al. (2019). Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1902. 03368"},{"key":"2776_CR8","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133223.","DOI":"10.1109\/CVPR.2016.350"},{"key":"2776_CR9","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1090\/S0025-5718-1993-1195422-2","volume":"61","author":"G-H Cottet","year":"1993","unstructured":"Cottet, G.-H., & Germain, L. E. (1993). Image processing through reaction combined with nonlinear diffusion. Mathematics of Computation, 61, 659\u2013673.","journal-title":"Mathematics of Computation"},{"key":"2776_CR10","doi-asserted-by":"crossref","unstructured":"Daudt, R.C., Saux, B.L., Boulch, A., & Gousseau, Y. (2019). Guided anisotropic diffusion and iterative learning for weakly supervised change detection. 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1461\u20131470.","DOI":"10.1109\/CVPRW.2019.00187"},{"issue":"2","key":"2776_CR11","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1016\/0022-247X(74)90025-0","volume":"47","author":"I Ekeland","year":"1974","unstructured":"Ekeland, I. (1974). On the variational principle. Journal of Mathematical Analysis and Applications, 47(2), 324\u2013353. https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1016\/0022-247X(74)90025-0","journal-title":"Journal of Mathematical Analysis and Applications"},{"issue":"1","key":"2776_CR12","first-page":"211","volume":"19","author":"LC Evans","year":"2010","unstructured":"Evans, L. C. (2010). Partial differential equations: Second edition. Wadsworth and Brooks\/cole Mathematics, 19(1), 211\u2013223.","journal-title":"Wadsworth and Brooks\/cole Mathematics"},{"key":"2776_CR13","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International journal of computer vision, 88, 303\u2013338.","journal-title":"International journal of computer vision"},{"key":"2776_CR14","doi-asserted-by":"publisher","unstructured":"Garea, S.A., & Das, S. (2024). Image segmentation methods: Overview, challenges, and future directions. In: 2024 Seventh International Women in Data Science Conference at Prince Sultan University (WiDS PSU), pp. 56\u201361. https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1109\/WiDS-PSU61003.2024.00026","DOI":"10.1109\/WiDS-PSU61003.2024.00026"},{"key":"2776_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.array.2021.100057","volume":"10","author":"A Gupta","year":"2021","unstructured":"Gupta, A., Anpalagan, A., Guan, L., & Khwaja, A. S. (2021). Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues. Array, 10, Article 100057. https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1016\/j.array.2021.100057","journal-title":"Array"},{"key":"2776_CR16","doi-asserted-by":"crossref","unstructured":"Henry, T., Carr\u00e9, A., Lerousseau, M., Estienne, T., Robert, C., Paragios, N., & Deutsch, E. (2021). Brain tumor segmentation with self-ensembled, deeply-supervised 3d u-net neural networks: a brats 2020 challenge solution. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I 6, pp. 327\u2013339. Springer","DOI":"10.1007\/978-3-030-72084-1_30"},{"key":"2776_CR17","doi-asserted-by":"crossref","unstructured":"Hou, Q., & Liu, F. (2019). Context-aware image matting for simultaneous foreground and alpha estimation. 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), 4129\u20134138.","DOI":"10.1109\/ICCV.2019.00423"},{"key":"2776_CR18","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/BF00932298","volume":"23","author":"AK Jain","year":"1977","unstructured":"Jain, A. K. (1977). Partial differential equations and finite-difference methods in image processing, part 1: Image representation. Journal of optimization Theory and Applications, 23, 65\u201391.","journal-title":"Journal of optimization Theory and Applications"},{"key":"2776_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120705","volume":"673","author":"S Jiang","year":"2024","unstructured":"Jiang, S., Wu, Z., Yang, H., Xiang, K., Ding, W., & Chen, Z.-S. (2024). A prior knowledge-guided distributionally robust optimization-based adversarial training strategy for medical image classification. Information Sciences, 673, Article 120705.","journal-title":"Information Sciences"},{"key":"2776_CR20","doi-asserted-by":"publisher","unstructured":"Karniadakis, G., Kevrekidis, Y., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021). Physics-informed machine learning. Nature Reviews Physics, 1\u201319. https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1038\/s42254-021-00314-5","DOI":"10.1038\/s42254-021-00314-5"},{"key":"2776_CR21","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1137\/S003613999529558X","volume":"57","author":"S Kichenassamy","year":"1997","unstructured":"Kichenassamy, S. (1997). The perona-malik paradox. SIAM J. Appl. Math., 57, 1328\u20131342.","journal-title":"SIAM J. Appl. Math."},{"issue":"5","key":"2776_CR22","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/BF00336961","volume":"50","author":"JJ Koenderink","year":"1984","unstructured":"Koenderink, J. J. (1984). The structure of images. Biological cybernetics, 50(5), 363\u2013370.","journal-title":"Biological cybernetics"},{"key":"2776_CR23","unstructured":"Li, H., Xu, Z., Taylor, G., Studer, C., & Goldstein, T. (2018). Visualizing the loss landscape of neural nets. In: NIPS."},{"key":"2776_CR24","doi-asserted-by":"crossref","unstructured":"Liang, P. (1998). Local scale controlled anisotropic diffusion with local noise estimate for image smoothing and edge detection. Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), 193\u2013200","DOI":"10.1109\/ICCV.1998.710718"},{"issue":"8","key":"2776_CR25","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1109\/TMI.2023.3250474","volume":"42","author":"J Lin","year":"2023","unstructured":"Lin, J., Lin, J., Lu, C., Chen, H., Lin, H., Zhao, B., Shi, Z., Qiu, B., Pan, X., Xu, Z., et al. (2023). Ckd-transbts: clinical knowledge-driven hybrid transformer with modality-correlated cross-attention for brain tumor segmentation. IEEE transactions on medical imaging, 42(8), 2451\u20132461.","journal-title":"IEEE transactions on medical imaging"},{"issue":"7","key":"2776_CR26","doi-asserted-by":"publisher","first-page":"3523","DOI":"10.1109\/TPAMI.2021.3059968","volume":"44","author":"S Minaee","year":"2022","unstructured":"Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., & Terzopoulos, D. (2022). Image segmentation using deep learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7), 3523\u20133542. https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1109\/TPAMI.2021.3059968","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2776_CR27","unstructured":"Minar, M. R., & Naher, J. (2018). Recent advances in deep learning: An overview. arXiv:abs\/1807.08169"},{"key":"2776_CR28","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1109\/34.149593","volume":"14","author":"M Nitzberg","year":"1992","unstructured":"Nitzberg, M., & Shiota, T. (1992). Nonlinear image filtering with edge and corner enhancement. IEEE Trans. Pattern Anal. Mach. Intell., 14, 826\u2013833.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2776_CR29","doi-asserted-by":"crossref","unstructured":"Pandey, R.K., Saha, N., Karmakar, S., & Ramakrishnan, A. G. (2018). Msce: An edge preserving robust loss function for improving super-resolution algorithms. In: International Conference on Neural Information Processing. https:\/\/linproxy.fan.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:52157577","DOI":"10.1007\/978-3-030-04224-0_49"},{"issue":"7","key":"2776_CR30","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1109\/34.56205","volume":"12","author":"P Perona","year":"1990","unstructured":"Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7), 629\u2013639. https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1109\/34.56205","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2776_CR31","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., & Marculescu, R. (2023). Medical image segmentation via cascaded attention decoding. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6222\u20136231.","DOI":"10.1109\/WACV56688.2023.00616"},{"key":"2776_CR32","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Munir, M., & Marculescu, R. (2024). Emcad: Efficient multi-scale convolutional attention decoding for medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11769\u201311779.","DOI":"10.1109\/CVPR52733.2024.01118"},{"key":"2776_CR33","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686\u2013707. https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1016\/j.jcp.2018.10.045","journal-title":"Journal of Computational Physics"},{"key":"2776_CR34","first-page":"234","volume-title":"Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In N. Navab, J. Hornegger, W. M. Wells, & A. F. Frangi (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 (pp. 234\u2013241). Cham: Springer."},{"key":"2776_CR35","doi-asserted-by":"crossref","unstructured":"Ruan, J., Li, J., & Xiang, S. (2024). Vm-unet: Vision mamba unet for medical image segmentation. arXiv preprint arXiv:2402.02491","DOI":"10.1145\/3767748"},{"key":"2776_CR36","doi-asserted-by":"crossref","unstructured":"Ruan, J., Xiang, S., Xie, M., Liu, T., & Fu, Y. (2022). Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1150\u20131156. IEEE","DOI":"10.1109\/BIBM55620.2022.9995040"},{"key":"2776_CR37","doi-asserted-by":"crossref","unstructured":"Ruan, J., Xie, M., Gao, J., Liu, T., & Fu, Y. (2023). Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 481\u2013490. Springer","DOI":"10.1007\/978-3-031-43901-8_46"},{"issue":"11","key":"2776_CR38","doi-asserted-by":"publisher","first-page":"1582","DOI":"10.1109\/83.541429","volume":"5","author":"G Sapiro","year":"1996","unstructured":"Sapiro, G., & Ringach, D. L. (1996). Anisotropic diffusion of multivalued images with applications to color filtering. IEEE transactions on image processing: a publication of the IEEE Signal Processing Society, 5(11), 1582\u20136.","journal-title":"IEEE transactions on image processing: a publication of the IEEE Signal Processing Society"},{"issue":"1","key":"2776_CR39","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1023\/A:1008344608808","volume":"12","author":"O Scherzer","year":"2000","unstructured":"Scherzer, O., & Weickert, J. (2000). Relations between regularization and diffusion filtering. J. Math. Imaging Vis., 12(1), 43\u201363. https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1023\/A:1008344608808","journal-title":"J. Math. Imaging Vis."},{"issue":"4","key":"2776_CR40","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer, E., Long, J., & Darrell, T. (2017). Fully convolutional networks for semantic segmentation. PAMI, 39(4), 640\u2013651. https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1109\/TPAMI.2016.2572683","journal-title":"PAMI"},{"issue":"7","key":"2776_CR41","doi-asserted-by":"publisher","first-page":"7401","DOI":"10.1109\/TITS.2023.3348631","volume":"25","author":"M Shi","year":"2024","unstructured":"Shi, M., Lin, S., Yi, Q., Weng, J., Luo, A., & Zhou, Y. (2024). Lightweight context-aware network using partial-channel transformation for real-time semantic segmentation. IEEE Transactions on Intelligent Transportation Systems, 25(7), 7401\u20137416.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"2776_CR42","unstructured":"Teichmann, M., & Cipolla, R. (2018). Convolutional crfs for semantic segmentation. In: British Machine Vision Conference. https:\/\/linproxy.fan.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:21655707"},{"key":"2776_CR43","unstructured":"Terven, J.R., C\u00f3rdova-Esparza, D.-M., Ram\u00edrez-Pedraza, A., Chavez-Urbiola, E.A., & Romero-Gonz\u00e1lez, J.-A. (2023). Loss functions and metrics in deep learning. arXiv preprint arXiv:1701. 07102"},{"key":"2776_CR44","doi-asserted-by":"crossref","unstructured":"Valanarasu, J.M.J., & Patel, V.M. (2022). Unext: Mlp-based rapid medical image segmentation network. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 23\u201333. Springer","DOI":"10.1007\/978-3-031-16443-9_3"},{"issue":"2","key":"2776_CR45","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1137\/070711797","volume":"7","author":"W-W Wang","year":"2008","unstructured":"Wang, W.-W., & Feng, X.-C. (2008). Anisotropic diffusion with nonlinear structure tensor. Multiscale Modeling & Simulation, 7(2), 963\u2013977.","journal-title":"Multiscale Modeling & Simulation"},{"key":"2776_CR46","unstructured":"Weickert, J. (1994). Theoretical foundations of anisotropic diffusion in image processing. In: Theoretical Foundations of Computer Vision. https:\/\/linproxy.fan.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:12705991"},{"issue":"1","key":"2776_CR47","first-page":"272","volume":"16","author":"J Weickert","year":"1998","unstructured":"Weickert, J. (1998). Anisotropic diffusion in image processing. B.g.teubner Stuttgart, 16(1), 272.","journal-title":"B.g.teubner Stuttgart"},{"key":"2776_CR48","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1023\/A:1008009714131","volume":"31","author":"J Weickert","year":"1999","unstructured":"Weickert, J. (1999). Coherence-enhancing diffusion filtering. International journal of computer vision, 31, 111\u2013127.","journal-title":"International journal of computer vision"},{"key":"2776_CR49","first-page":"103","volume":"13","author":"J Weickert","year":"2002","unstructured":"Weickert, J., & Scharr, H. (2002). A scheme for coherence-enhancing diffusion filtering with optimized rotation invariance. J. Weickert. H. Scharr, 13, 103\u2013118.","journal-title":"H. Scharr"},{"key":"2776_CR50","doi-asserted-by":"crossref","unstructured":"Weikert, J. (1995). Multiscale texture enhancement. In: International Conference on Computer Analysis of Images and Patterns. https:\/\/linproxy.fan.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:5513089","DOI":"10.1007\/3-540-60268-2_301"},{"key":"2776_CR51","doi-asserted-by":"crossref","unstructured":"Welk, M., Theis, D., Brox, T., & Weickert, J. (2005). Pde-based deconvolution with forward-backward diffusivities and diffusion tensors. In: Scale-Space. https:\/\/linproxy.fan.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:1724120","DOI":"10.1007\/11408031_50"},{"key":"2776_CR52","doi-asserted-by":"crossref","unstructured":"Wenxuan, W., Chen, C., Meng, D., Hong, Y., Sen, Z., & Jiangyun, L. (2021). Transbts: Multimodal brain tumor segmentation using transformer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 109\u2013119","DOI":"10.1007\/978-3-030-87193-2_11"},{"key":"2776_CR53","doi-asserted-by":"crossref","unstructured":"Witkin, A. P. (1987). Scale-space filtering. In: Readings in Computer Vision, pp. 329\u2013332. Elsevier, Amsterdam, Netherlands.","DOI":"10.1016\/B978-0-08-051581-6.50036-2"},{"key":"2776_CR54","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., \u00c1lvarez, J.M., & Luo, P. (2021). Segformer: Simple and efficient design for semantic segmentation with transformers. In: Neural Information Processing Systems. https:\/\/linproxy.fan.workers.dev:443\/https\/api.semanticscholar.org\/CorpusID:235254713"},{"key":"2776_CR55","doi-asserted-by":"crossref","unstructured":"Xu, J., Xiong, Z., & Bhattacharyya, S.P. (2023). Pidnet: A real-time semantic segmentation network inspired by pid controllers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19529\u201319539.","DOI":"10.1109\/CVPR52729.2023.01871"},{"key":"2776_CR56","doi-asserted-by":"crossref","unstructured":"Yao, T., Qu, C., Liu, Q., Deng, R., Tian, Y., Xu, J., Jha, A., Bao, S., Zhao, M., Fogo, A.B., et al. (2021). Compound figure separation of biomedical images with side loss. In: Proceedings of the Deep Generative Models, and Data Augmentation, Labelling, and Imperfections, Strasbourg, France, 1 October, vol. 13003, pp. 173\u2013183","DOI":"10.1007\/978-3-030-88210-5_16"},{"issue":"5","key":"2776_CR57","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.3390\/electronics12051199","volume":"12","author":"Y Yu","year":"2023","unstructured":"Yu, Y., Wang, C., Fu, Q., Kou, R., Huang, F., Yang, B., Yang, T., & Gao, M. (2023). Techniques and challenges of image segmentation: A review. Electronics, 12(5), 1199.","journal-title":"Electronics"},{"key":"2776_CR58","doi-asserted-by":"crossref","unstructured":"Zhang, M., Bai, H., Zhang, J., Zhang, R., Wang, C., Guo, J., & Gao, X. (2022). Rkformer: Runge-kutta transformer with random-connection attention for infrared small target detection. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 1730\u20131738.","DOI":"10.1145\/3503161.3547817"},{"key":"2776_CR59","doi-asserted-by":"crossref","unstructured":"Zhang, M., Yang, H., Guo, J., Li, Y., Gao, X., & Zhang, J. (2024). Irprunedet: efficient infrared small target detection via wavelet structure-regularized soft channel pruning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 7224\u20137232.","DOI":"10.1609\/aaai.v38i7.28551"},{"key":"2776_CR60","doi-asserted-by":"crossref","unstructured":"Zhang, M., Yue, K., Zhang, J., Li, Y., & Gao, X. (2022). Exploring feature compensation and cross-level correlation for infrared small target detection. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 1857\u20131865.","DOI":"10.1145\/3503161.3548264"},{"key":"2776_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, R., Yang, Y., Bai, H., Zhang, J., & Guo, J. (2022). Isnet: Shape matters for infrared small target detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 877\u2013886.","DOI":"10.1109\/CVPR52688.2022.00095"},{"key":"2776_CR62","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, C., Zhang, Q., Li, Y., Gao, X., & Zhang, J. (2024). Unleashing the power of generic segmentation model: A simple baseline for infrared small target detection. In: Proceedings of the 32nd ACM International Conference on Multimedia, pp. 10392\u201310401.","DOI":"10.1145\/3664647.3680609"},{"key":"2776_CR63","doi-asserted-by":"crossref","unstructured":"Zhang, M., Wang, Y., Guo, J., Li, Y., Gao, X., & Zhang, J. (2025). Irsam: Advancing segment anything model for infrared small target detection. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), Computer Vision - ECCV 2024 (pp. 233\u2013249). Cham: Springer.","DOI":"10.1007\/978-3-031-72855-6_14"},{"key":"2776_CR64","doi-asserted-by":"crossref","unstructured":"Zhao, M., Liu, Q., Jha, R., Deng, R., Yao, T., Mahadevan, J., Tyska, M.J., Millis, B.A., & Huo, Y. (2022). Voxelembed: 3d instance segmentation and tracking with voxel embedding based deep learning. In: Proceedings of the International Workshop on Machine Learning in Medical Imaging, Strasbourg, France, 27 September, vol. 12966, pp. 437\u2013446.","DOI":"10.1007\/978-3-030-87589-3_45"},{"key":"2776_CR65","doi-asserted-by":"publisher","first-page":"15844","DOI":"10.1109\/ACCESS.2018.2810849","volume":"6","author":"Q Zheng","year":"2018","unstructured":"Zheng, Q., Yang, M., Yang, J., Zhang, Q., & Zhang, X. (2018). Improvement of generalization ability of deep cnn via implicit regularization in two-stage training process. IEEE Access, 6, 15844\u201315869. https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1109\/ACCESS.2018.2810849","journal-title":"IEEE Access"},{"key":"2776_CR66","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman\u00a0Siddiquee, M.M., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pp. 3\u201311. Springer","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"2776_CR67","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Sun, M., Qi, G., Li, Y., Gao, X., & Liu, Y. (2024). Sparse dynamic volume transunet with multi-level edge fusion for brain tumor segmentation. Computers in Biology and Medicine, 108284.","DOI":"10.1016\/j.compbiomed.2024.108284"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02776-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/link.springer.com\/article\/10.1007\/s11263-026-02776-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02776-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T08:38:07Z","timestamp":1774600687000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/link.springer.com\/10.1007\/s11263-026-02776-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,22]]},"references-count":67,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["2776"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1007\/s11263-026-02776-5","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,22]]},"assertion":[{"value":"12 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"137"}}