{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:52:21Z","timestamp":1781765541215,"version":"3.54.5"},"reference-count":49,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Accurate breast lesion segmentation using deep learning models requires precise understanding of both global contextual relevance and finer lesion structure details, which remains a challenge for existing convolutional and transformer-based approaches. This study aims to address these limitations by proposing a new segmentation model capable of improving context-aware dense segmentation tasks for ultrasound images. Method: We propose LTM-UNet, a novel segmentation method integrating transformer-based encoding with state-space-driven decoding in a U-Net-style framework. The architecture utilizes an efficient vision transformer encoder to extract multi-scale global representations. These features are refined through an attention-guided skip-fusion mechanism incorporating spatial-channel attention preserving finer spatial details and thereby minimizes the semantic gap between encoder and decoder features. Additionally, a direction-aware decoder based on a state-space model is introduced to efficiently capture long-range dependencies and enhance relevant feature reconstruction. Results: Extensive experiments on benchmark ultrasound medical imaging datasets demonstrate the effectiveness of the proposed method. The model achieves dice-score coefficients of 82.41% on the BUSI dataset and 86.62% on Dataset B (UDIAT), outperforming several existing segmentation approaches in both dice-score coefficient and Intersection-over-Union (IoU) metrics. Conclusions: The integration of efficient transformer-based global feature extraction, attention-enhanced feature fusion, and state-space-driven decoding enables LTM-UNet to effectively capture both structural details and contextual information, resulting in superior segmentation performance compared to existing methods.<\/jats:p>","DOI":"10.3390\/diagnostics16121888","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T15:11:22Z","timestamp":1781709082000},"page":"1888","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LTM-UNet: Linear Transformer\u2013Mamba with Attention-Based U-Net for Context-Aware Breast Ultrasound Image Segmentation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5060-8183","authenticated-orcid":false,"given":"Shivpratap","family":"Kushwah","sequence":"first","affiliation":[{"name":"Department of Information Technology, Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior 474015, MP, India"},{"name":"Centre for Biomedical Research, Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior 474015, MP, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0817-1798","authenticated-orcid":false,"given":"Santosh","family":"Chouhan","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior 474015, MP, India"},{"name":"Centre for Biomedical Research, Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior 474015, MP, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1175-1865","authenticated-orcid":false,"given":"Narinder","family":"Punn","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior 474015, MP, India"},{"name":"Centre for Biomedical Research, Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior 474015, MP, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3996-9818","authenticated-orcid":false,"given":"Mahua","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior 474015, MP, India"},{"name":"Centre for Biomedical Research, Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior 474015, MP, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"ref_1","first-page":"101767","article-title":"Comparative study of mammography and breast ultrasound in the diagnosis of breast cancer in young women: A meta-analysis","volume":"18","author":"Jiang","year":"2025","journal-title":"J. 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