{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T05:00:10Z","timestamp":1785387610506,"version":"3.55.0"},"reference-count":48,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Reliable medical image segmentation remains challenging because models must preserve fine boundary details while maintaining global semantic consistency. CNNs capture local structures effectively but have limited long-range modeling ability, whereas Transformer-based methods improve global context at high computational cost. Mamba-based state space models offer efficient long-range modeling, but may weaken high-frequency textures and boundary cues. To address these limitations, we propose DIG-MambaNet, a Dual-path Interactive Guided Mamba Network for medical image segmentation. The network introduces a dual-path complementary modeling block (DCM Block), where a cross-feature spatial interaction module (CSIM) adaptively integrates CNN-based local features and Mamba-based global features. A source image-guided module (SIGM) injects high-frequency information from the original image to compensate for downsampling-induced detail loss, while an inter-layer detail refinement fusion module (IDRFM) improves encoder\u2013decoder feature alignment during reconstruction. Experiments on 2018DSB, ISIC2018, JSUAH-Cerebellum, and CVC-ClinicDB, covering nuclei segmentation in microscopy images, skin lesion segmentation in dermoscopic images, fetal cerebellum segmentation in ultrasound images, and polyp segmentation in colonoscopy images, demonstrate that DIG-MambaNet achieves consistent and competitive performance across diverse target structures and imaging conditions, with improved boundary delineation and favorable overlap-based accuracy compared with representative CNN-, Transformer-, and Mamba-based methods.<\/jats:p>","DOI":"10.3390\/jimaging12080343","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T15:57:28Z","timestamp":1785254248000},"page":"343","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DIG-MambaNet: A Dual-Path Interactive Guided Mamba Network for Medical Image Segmentation"],"prefix":"10.3390","volume":"12","author":[{"given":"Yongkang","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyue","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongmei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1079-8434","authenticated-orcid":false,"given":"Xin","family":"Shu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A Survey on Deep Learning in Medical Image Analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_2","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","volume":"Volume 9351","author":"Navab","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015"},{"key":"ref_3","first-page":"3","article-title":"UNet++: A nested U-Net architecture for medical image segmentation","volume":"Volume 11045","author":"Stoyanov","year":"2018","journal-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support"},{"key":"ref_4","unstructured":"Oktay, O., Schlemper, J., Le Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention U-Net: Learning where to look for the pancreas. arXiv."},{"key":"ref_5","first-page":"4898","article-title":"Understanding the effective receptive field in deep convolutional neural networks","volume":"29","author":"Luo","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"109097","DOI":"10.1016\/j.bspc.2025.109097","article-title":"CASA-Net: A context-aware and self-attention network for medical image segmentation","volume":"113","author":"Xie","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"112990","DOI":"10.1016\/j.asoc.2025.112990","article-title":"Adaptive encoding and comprehensive attention decoding network for medical image segmentation","volume":"174","author":"Shu","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"114760","DOI":"10.1016\/j.asoc.2026.114760","article-title":"Hierarchical attentive feature refinement network with cross-resolution detail preservation for medical image segmentation","volume":"192","author":"Shu","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"ref_9","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_10","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_11","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). TransUNet: Transformers make strong encoders for medical image segmentation. arXiv."},{"key":"ref_12","first-page":"205","article-title":"Swin-Unet: Unet-like pure Transformer for medical image segmentation","volume":"Volume 13803","author":"Karlinsky","year":"2023","journal-title":"Computer Vision\u2014ECCV 2022 Workshops"},{"key":"ref_13","first-page":"36","article-title":"Medical Transformer: Gated axial-attention for medical image segmentation","volume":"Volume 12901","author":"Cattin","year":"2021","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021"},{"key":"ref_14","unstructured":"Gu, A., and Dao, T. (2024, January 7\u20139). Mamba: Linear-time sequence modeling with selective state spaces. Proceedings of the First Conference on Language Modeling, Philadelphia, PA, USA."},{"key":"ref_15","unstructured":"Ma, J., Li, F., and Wang, B. (2024). U-Mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv."},{"key":"ref_16","unstructured":"Yue, Y., and Li, Z. (2024). MedMamba: Vision Mamba for medical image classification. arXiv."},{"key":"ref_17","unstructured":"Xu, J. (2024). HC-Mamba: Vision Mamba with hybrid convolutional techniques for medical image segmentation. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision, Stanford, CA, USA, 25\u201328 October 2016, IEEE.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_21","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_25","first-page":"421","article-title":"Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks","volume":"Volume 11070","author":"Frangi","year":"2018","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2018"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Misra, D., Nalamada, T., Arasanipalai, A.U., and Hou, Q. (2021, January 5\u20139). Rotate to Attend: Convolutional Triplet Attention Module. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00318"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_28","first-page":"2441","article-title":"UCTransNet: Rethinking the skip connections in U-Net from a channel-wise perspective with Transformer","volume":"36","author":"Wang","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical vision Transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., and Shao, L. (2021, January 10\u201317). Pyramid Vision Transformer: A versatile backbone for dense prediction without convolutions. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref_31","first-page":"62429","article-title":"Vision Mamba: Efficient visual representation learning with bidirectional state space model","volume":"235","author":"Zhu","year":"2024","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"103031","DOI":"10.52202\/079017-3273","article-title":"VMamba: Visual state space model","volume":"37","author":"Liu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ruan, J., Li, J., and Xiang, S. (2025). VM-UNet: Vision Mamba UNet for medical image segmentation. ACM Trans. Multimed. Comput. Commun. Appl.","DOI":"10.1145\/3767748"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, C., Yu, L., Min, S., and Wang, S. (2024, January 3\u20136). MSVM-UNet: Multi-scale Vision Mamba UNet for medical image segmentation. Proceedings of the 2024 IEEE International Conference on Bioinformatics and Biomedicine, Lisbon, Portugal.","DOI":"10.1109\/BIBM62325.2024.10821761"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"129447","DOI":"10.1016\/j.neucom.2025.129447","article-title":"H-vmunet: High-order Vision Mamba UNet for medical image segmentation","volume":"624","author":"Wu","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"101298","DOI":"10.1016\/j.patter.2025.101298","article-title":"UltraLight VM-UNet: Parallel Vision Mamba significantly reduces parameters for skin lesion segmentation","volume":"6","author":"Wu","year":"2025","journal-title":"Patterns"},{"key":"ref_37","first-page":"615","article-title":"Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining","volume":"Volume 15009","author":"Linguraru","year":"2024","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2024"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"123430","DOI":"10.1016\/j.eswa.2024.123430","article-title":"MPFC-Net: A multi-perspective feature compensation network for medical image segmentation","volume":"248","author":"Wu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_39","first-page":"12321","article-title":"F3Net: Fusion, feedback and focus for salient object detection","volume":"34","author":"Wei","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., and Jagersand, M. (2019, January 15\u201320). BASNet: Boundary-aware salient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00766"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1109\/TMI.2021.3111679","article-title":"Genetic U-Net: Automatically designed deep networks for retinal vessel segmentation using a genetic algorithm","volume":"41","author":"Wei","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1038\/s41592-019-0612-7","article-title":"Nucleus segmentation across imaging experiments: The 2018 Data Science Bowl","volume":"16","author":"Caicedo","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"180161","DOI":"10.1038\/sdata.2018.161","article-title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","volume":"5","author":"Tschandl","year":"2018","journal-title":"Sci. Data"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"105693","DOI":"10.1016\/j.compbiomed.2022.105693","article-title":"FCRB U-Net: A novel fully connected residual block U-Net for fetal cerebellum ultrasound image segmentation","volume":"148","author":"Shu","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","article-title":"WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians","volume":"43","author":"Bernal","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/978-3-031-76197-3_2","article-title":"AC-MambaSeg: An adaptive convolution and Mamba-based architecture for enhanced skin lesion segmentation","volume":"Volume 1195","author":"Huang","year":"2024","journal-title":"Computational Intelligence Methods for Green Technology and Sustainable Development"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"108584","DOI":"10.1016\/j.bspc.2025.108584","article-title":"MFEVM-UNet: Multi-scale feature fusion and enhancement Vision Mamba UNet for medical image segmentation","volume":"112","author":"Guo","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/8\/343\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T04:36:48Z","timestamp":1785386208000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/8\/343"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,28]]},"references-count":48,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["jimaging12080343"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12080343","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,28]]}}}