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Oncol."],"abstract":"<jats:p>Medical image segmentation is fundamental for disease diagnosis, particularly in the context of breast cancer, a prevalent malignancy affecting women. The accuracy of lesion localization and preservation of image details are essential for ensuring the integrity of lesion segmentation. However, the low resolution of breast tumor B-mode ultrasound images poses challenges in precisely identifying lesion sites. To address this issue, this study introduces the Mamba architecture model, which combines three foundational models with the long-sequence processing model Mamba to develop a novel segmentation model for breast tumor ultrasound images. The selective mechanism and hardware-aware algorithm of the Mamba model enable longer sequence inputs and faster computing speeds. Moreover, integrating a complete chain of VMamba blocks into the basic model enhances segmentation accuracy and image detail processing capabilities. Experimental segmentation was performed on two benchmark ultrasound datasets (BUSI and BUS-BRA) using both the baseline and improved models. The results were compared using metrics such as Dice and IoU, with additional evaluations conducted under small-sample training conditions. This study is intended to provide guidance for the future development of medical image segmentation. Moreover, the experimental results demonstrate that the model incorporating the Mamba architecture achieves superior performance on breast ultrasound images.<\/jats:p>","DOI":"10.3389\/fonc.2025.1672274","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T05:14:47Z","timestamp":1763961287000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Research on breast tumor segmentation based on the Mamba architecture"],"prefix":"10.3389","volume":"15","author":[{"given":"Weihao","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiacheng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangming","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"B1","first-page":"1","article-title":"Breast tumor segmentation using image segmentation algorithms","author":"Roy","year":"2019"},{"key":"B2","first-page":"1","article-title":"Segmentation and classification of benign and Malignant breast tumors via texture characterization from ultrasound images","author":"Benaouali","year":"2022"},{"key":"B3","doi-asserted-by":"crossref","DOI":"10.1109\/EMBC.2019.8857438","article-title":"Morphological characterization of breast tumors using conventional b-mode ultrasound images","author":"El-Azizy","year":"2019"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1001\/jama.293.10.1245","article-title":"Screening for breast cancer","volume":"293","author":"Elmore","year":"2005","journal-title":"Jama"},{"key":"B5","article-title":"Medical image segmentation: a review","volume":"2","author":"Patil","year":"2013","journal-title":"Int J Comput Sci Mobile Computing"},{"key":"B6","doi-asserted-by":"publisher","first-page":"23","DOI":"10.5121\/sipij.2012.3403","article-title":"Histopathological image analysis using image processing techniques: An overview","volume":"3","author":"Belsare","year":"2012","journal-title":"Signal Image Process"},{"key":"B7","article-title":"Imagenet classification with deep convolutional neural networks","volume-title":"Advances in Neural Information Processing Systems","author":"Krizhevsky","year":"2012"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3128607","article-title":"Aedcn-net: Accurate and efficient deep convolutional neural network model for medical image segmentation","volume":"9","author":"Olimov","year":"2021","journal-title":"IEEE Access"},{"key":"B9","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.jare.2021.03.015","article-title":"A review on modern defect detection models using dcnns\u2013deep convolutional neural networks","volume":"35","author":"Tulbure","year":"2022","journal-title":"J Advanced Res"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1007\/s42600-021-00137-4","article-title":"U-net and segnet performances on lesion segmentation of breast ultrasonography images","volume":"37","author":"Vianna","year":"2021","journal-title":"Res Biomed Eng"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2845918","article-title":"H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes","volume":"37","author":"Li","year":"2018","journal-title":"IEEE Trans Med Imaging"},{"key":"B12","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2017.243","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"B13","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"B14","article-title":"CBAM: Convolutional Block Attention Module","volume-title":"Computer Vision - ECCV 2018. 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