{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T18:25:56Z","timestamp":1774290356233,"version":"3.50.1"},"reference-count":39,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T00:00:00Z","timestamp":1774224000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guizhou Provincial Natural Science Research Project","award":["Qian Jiao Ji[2024]279"],"award-info":[{"award-number":["Qian Jiao Ji[2024]279"]}]},{"name":"Guizhou Provincial Education Science Planning Project","award":["Salary (2024C018)"],"award-info":[{"award-number":["Salary (2024C018)"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Accurate segmentation of lesions in prostate magnetic resonance images (MRI) is important for assessing patient health and personalized treatment in the clinic. However, the traditional UNet segmentation network has low segmentation accuracy because of the fuzzy boundary and low contrast. Therefore, we propose a Lightweight Mamba-UNet (LM-UNet) prostate MRI image segmentation method. Initially, the encoder-decoder backbone structure consists of parallel vision mamba (PV-Mamba) and efficient multi-scale attention (EMA). The number of model parameters is reduced by constructing PV-Mamba while extracting the correlation between features over long distances. The EMA is then used to learn different spatial features in groups and construct cross-spatial information aggregation methods for richer feature aggregation. Subsequently, we construct the edge feature extraction (EFE) and the edge feature fusion (EFF) to achieve different levels of feature fusion in the encoder. Ultimately, we suggest a multi-stage and multi-level skip connections (MMSC) to achieve multi-level fusion between the encoder and decoder, there reducing semantic discrepancies between contextual features and improving segmentation accuracy. Experimental results demonstrate that on the PROMISE12 dataset, LM-UNet outperforms seven comparative segmentation methods in terms of parameter count, computational memory requirements, and precise segmentation of lesion margins.<\/jats:p>","DOI":"10.1371\/journal.pone.0339719","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T17:32:51Z","timestamp":1774287171000},"page":"e0339719","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["LM-UNet: Lightweight Mamba-UNet Prostate MRI image segmentation network"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1613-3687","authenticated-orcid":true,"given":"Kuncai","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yilong","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,3,23]]},"reference":[{"issue":"1","key":"pone.0339719.ref001","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.eururo.2019.08.005","article-title":"Recent Global Patterns in Prostate Cancer Incidence and Mortality Rates","volume":"77","author":"MB Culp","year":"2020","journal-title":"Eur Urol"},{"issue":"4","key":"pone.0339719.ref002","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11571-020-09587-5","article-title":"Detecting prostate cancer using deep learning convolution neural network with transfer learning approach","volume":"14","author":"AA Abbasi","year":"2020","journal-title":"Cogn Neurodyn"},{"key":"pone.0339719.ref003","doi-asserted-by":"crossref","first-page":"719","DOI":"10.3389\/fphar.2020.00719","article-title":"Health Care Utilization and Costs of Patients With Prostate Cancer in China Based on National Health Insurance Database From 2015 to 2017","volume":"11","author":"L Bai","year":"2020","journal-title":"Front Pharmacol"},{"issue":"2","key":"pone.0339719.ref004","article-title":"Effects of laparoscopic radical prostatectomy on wound infection of surgery in patients with prostate cancer: A meta-analysis","volume":"21","author":"C Li","year":"2024","journal-title":"International Wound Journal"},{"issue":"1","key":"pone.0339719.ref005","doi-asserted-by":"crossref","first-page":"26338","DOI":"10.1038\/s41598-024-77863-z","article-title":"Prevalence and associated risk factors of prostate cancer among a large Chinese population","volume":"14","author":"J Chen","year":"2024","journal-title":"Sci Rep"},{"key":"pone.0339719.ref006","doi-asserted-by":"crossref","first-page":"107999","DOI":"10.1016\/j.compbiomed.2024.107999","article-title":"ParaCM-PNet: A CNN-tokenized MLP combined parallel dual pyramid network for prostate and prostate cancer segmentation in MRI","volume":"170","author":"W Wang","year":"2024","journal-title":"Comput Biol Med"},{"issue":"3","key":"pone.0339719.ref007","doi-asserted-by":"crossref","DOI":"10.2214\/AJR.16.17551","article-title":"Interreader Agreement of Prostate Imaging Reporting and Data System Version 2 Using an In-Bore MRI-Guided Prostate Biopsy Cohort: A Single Institution\u2019s Initial Experience","volume":"209","author":"DI Glazer","year":"2017","journal-title":"AJR Am J Roentgenol"},{"issue":"5","key":"pone.0339719.ref008","doi-asserted-by":"crossref","first-page":"405","DOI":"10.4111\/icu.20250208","article-title":"Enhancement of inter-\/intra-reader agreement using the Prostate Imaging Reporting and Data System version 2.1 for prostate cancer detection in magnetic resonance imaging\/transrectal ultrasound software fusion prostate biopsy","volume":"66","author":"WH Song","year":"2025","journal-title":"Investig Clin Urol"},{"issue":"7","key":"pone.0339719.ref009","first-page":"3523","article-title":"Image Segmentation Using Deep Learning: A Survey","volume":"44","author":"S Minaee","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"01","key":"pone.0339719.ref010","first-page":"236","article-title":"Classifying invasive ductal carcinoma using transfer learning: An efficient methodology","volume":"4","author":"HI Younas","year":"2022","journal-title":"Journal of Computing and Biomedical Informatics"},{"key":"pone.0339719.ref011","article-title":"Advancing glioblastoma diagnosis through innovative deep learning image analysis in histopathology","author":"UB Zafar","year":"2024","journal-title":"Journal of Computing and Biomedical Informatics"},{"key":"pone.0339719.ref012","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III","author":"O Ronneberger","year":"2015"},{"issue":"9","key":"pone.0339719.ref013","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1109\/TMI.2022.3162111","article-title":"Global and Local Feature Reconstruction for Medical Image Segmentation","volume":"41","author":"J Song","year":"2022","journal-title":"IEEE Trans Med Imaging"},{"key":"pone.0339719.ref014","doi-asserted-by":"crossref","first-page":"116106","DOI":"10.1109\/ACCESS.2020.3002835","article-title":"Deep Guidance Network for Biomedical Image Segmentation","volume":"8","author":"P Yin","year":"2020","journal-title":"IEEE Access"},{"key":"pone.0339719.ref015","unstructured":"Asadi-Aghbolaghi M, Azad R, Fathy M, Escalera S. Multi-level context gating of embedded collective knowledge for medical image segmentation. arXiv preprint. 2020. https:\/\/doi.org\/arXiv:200305056"},{"issue":"3","key":"pone.0339719.ref016","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1109\/TMI.2019.2935018","article-title":"Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation","volume":"39","author":"Q Zhu","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"pone.0339719.ref017","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/TMI.2020.3025517","article-title":"Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy","volume":"40","author":"S Wang","year":"2021","journal-title":"IEEE Trans Med Imaging"},{"key":"pone.0339719.ref018","doi-asserted-by":"crossref","unstructured":"Zhang Y, Liu H, Hu Q. TransFuse: Fusing transformers and CNNs for medical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I, 2021. 14\u201324.","DOI":"10.1007\/978-3-030-87193-2_2"},{"issue":"1","key":"pone.0339719.ref019","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/TMI.2022.3211764","article-title":"CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI","volume":"42","author":"ALY Hung","year":"2023","journal-title":"IEEE Trans Med Imaging"},{"key":"pone.0339719.ref020","doi-asserted-by":"crossref","unstructured":"Pollastri F, Cipriano M, Bolelli F, Grana C. Long-Range 3D Self-Attention for MRI Prostate Segmentation. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022. 1\u20135. https:\/\/doi.org\/10.1109\/isbi52829.2022.9761448","DOI":"10.1109\/ISBI52829.2022.9761448"},{"issue":"12","key":"pone.0339719.ref021","doi-asserted-by":"crossref","first-page":"10138","DOI":"10.1109\/TPAMI.2024.3434373","article-title":"Transformer-Based Visual Segmentation: A Survey","volume":"46","author":"X Li","year":"2024","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"pone.0339719.ref022","doi-asserted-by":"crossref","unstructured":"Strudel R, Garcia R, Laptev I, Schmid C. Segmenter: Transformer for Semantic Segmentation. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), 2021. 7242\u201352. https:\/\/doi.org\/10.1109\/iccv48922.2021.00717","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"pone.0339719.ref023","unstructured":"Ma J, Li F, Wang B. U-Mamba: Enhancing long-range dependency for biomedical image segmentation. 2024. https:\/\/arxiv.org\/abs\/240104722"},{"key":"pone.0339719.ref024","doi-asserted-by":"crossref","unstructured":"Wang Z, Zheng JQ, Zhang Y, Cui G, Li L. Mamba-UNet: UNet-like pure visual mamba for medical image segmentation. 2024. https:\/\/arxiv.org\/abs\/240205079","DOI":"10.2139\/ssrn.5097998"},{"key":"pone.0339719.ref025","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":"R Wu","year":"2025","journal-title":"Neurocomputing"},{"key":"pone.0339719.ref026","first-page":"1","article-title":"Lightweight intrusion detection for IoD infrastructure using deep learning","author":"HMS Badar","year":"2024","journal-title":"Journal of Computing & Biomedical Informatics"},{"issue":"2","key":"pone.0339719.ref027","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.media.2013.12.002","article-title":"Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge","volume":"18","author":"G Litjens","year":"2014","journal-title":"Med Image Anal"},{"key":"pone.0339719.ref028","doi-asserted-by":"crossref","unstructured":"Wu R, Liu Y, Liang P, Chang Q. Ultralight VM-UNet: Parallel vision Mamba significantly reduces parameters for skin lesion segmentation. 2024. https:\/\/arxiv.org\/abs\/240320035","DOI":"10.1016\/j.patter.2025.101298"},{"key":"pone.0339719.ref029","unstructured":"Liao W, Zhu Y, Wang X, Pan C, Wang Y, Ma L. LightM-UNet: Mamba assists in lightweight UNet for medical image segmentation. arXiv preprint. 2024. https:\/\/doi.org\/arXiv:240305246"},{"key":"pone.0339719.ref030","doi-asserted-by":"crossref","unstructured":"Ouyang D, He S, Zhang G, Luo M, Guo H, Zhan J, et al. Efficient Multi-Scale Attention Module with Cross-Spatial Learning. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. 1\u20135. https:\/\/doi.org\/10.1109\/icassp49357.2023.10096516","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"pone.0339719.ref031","doi-asserted-by":"crossref","unstructured":"Sun Y, Wang S, Chen C, Xiang TZ. Boundary-guided camouflaged object detection. In: 2022. https:\/\/arxiv.org\/abs\/220700794","DOI":"10.24963\/ijcai.2022\/186"},{"key":"pone.0339719.ref032","doi-asserted-by":"crossref","first-page":"105517","DOI":"10.1016\/j.bspc.2023.105517","article-title":"MHorUNet: High-order spatial interaction UNet for skin lesion segmentation","volume":"88","author":"R Wu","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"key":"pone.0339719.ref033","doi-asserted-by":"crossref","unstructured":"Puttagunta RS, Kathariya B, Li Z, York G. Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal Image Super-Resolution. In: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024.","DOI":"10.1109\/CVPRW63382.2024.00314"},{"key":"pone.0339719.ref034","doi-asserted-by":"crossref","first-page":"201752","DOI":"10.1109\/ACCESS.2020.3035886","article-title":"Additive Angular Margin Loss in Deep Graph Neural Network Classifier for Learning Graph Edit Distance","volume":"8","author":"NI Kajla","year":"2020","journal-title":"IEEE Access"},{"key":"pone.0339719.ref035","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"FI Diakogiannis","year":"2020","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"pone.0339719.ref036","first-page":"263","volume-title":"PraNet: Parallel reverse attention network for polyp segmentation. In: Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2020","author":"DP Fan","year":"2020"},{"key":"pone.0339719.ref037","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y. TransUNet: Transformers make strong encoders for medical image segmentation. 2021. https:\/\/doi.org\/10.48550\/arXiv.2102.04306"},{"key":"pone.0339719.ref038","first-page":"205","volume-title":"Swin-UNet: UNet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision (ECCV","author":"H Cao","year":"2022"},{"key":"pone.0339719.ref039","doi-asserted-by":"crossref","unstructured":"Ruan J, Li J, Xiang S. VM-UNet: Vision mamba UNet for medical image segmentation. arXiv preprint. 2024. https:\/\/doi.org\/arXiv:240202491","DOI":"10.1145\/3767748"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0339719","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T17:32:59Z","timestamp":1774287179000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0339719"}},"subtitle":[],"editor":[{"given":"Kumaradevan","family":"Punithakumar","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2026,3,23]]},"references-count":39,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3,23]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0339719","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,23]]}}}