{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T03:03:01Z","timestamp":1784602981153,"version":"3.55.0"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Medical image segmentation plays a crucial role in clinical diagnosis, lesion quantification, and preoperative planning. However, existing Mamba-based architectures, which rely on fixed-direction sequence modeling and flatten images into one-dimensional (1D) sequences, struggle to capture hierarchical anatomical features and spatial dependencies, thereby limiting their representational capacity for complex medical structures. To address these limitations, we propose EccoMamba (Enhanced Cross-hierarchical Continuity Orthogonal Mamba), a U-shaped encoder--decoder framework designed for medical image segmentation. In the encoder's downsampling path, we introduce a Hierarchical Aggregation Enhancement (HAE) module that integrates multi-scale convolutions with hierarchical attention mechanisms. The attention branch further incorporates cross-channel interactions, allowing the model to selectively enhance semantically relevant features while suppressing irrelevant background responses. For skip connections, we design a Structural Continuity Orthogonal (SCO) module to preserve spatial continuity by modeling cross-dimensional dependencies via orthogonal Axial Shifts (AS), thereby mitigating directional bias and improving anatomical consistency. Extensive experiments on four benchmark datasets---ISIC 2018, ISIC 2017, Synapse, and ACDC---show that EccoMamba consistently outperforms state-of-the-art methods in both segmentation accuracy and structural fidelity.<\/jats:p>","DOI":"10.1609\/aaai.v40i13.38109","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:04:00Z","timestamp":1773792240000},"page":"11287-11295","source":"Crossref","is-referenced-by-count":1,"title":["EccoMamba: Enhanced Cross-hierarchical Continuity Orthogonal Mamba for Medical Image Segmentation"],"prefix":"10.1609","volume":"40","author":[{"given":"Junlin","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jincan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feifei","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialiang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuting","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiangguo","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajie","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38109\/42071","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38109\/42071","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:04:01Z","timestamp":1773792241000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38109"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i13.38109","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}