{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:15:19Z","timestamp":1784297719174,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"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>This study introduces a novel enhancement to the UNet architecture, termed Cascaded Spatial and Depth Attention U-Net (CSDA-UNet), tailored specifically for precise hippocampus segmentation in T1-weighted brain MRI scans. The proposed architecture integrates two key attention mechanisms: a Spatial Attention (SA) module, which refines spatial feature representations by producing attention maps from the deepest convolutional layer and modulating the matching object features; and an Inter-Slice Attention (ISA) module, which enhances volumetric uniformity by integrating related information from adjacent slices, thereby reinforcing the model\u2019s capacity to capture inter-slice dependencies. The CSDA-UNet is assessed using hippocampal segmentation data derived from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) and Decathlon, two benchmark studies widely employed in neuroimaging research. The proposed model outperforms state-of-the-art methods, achieving a Dice coefficient of 0.9512 and an IoU score of 0.9345 on ADNI and Dice scores of 0.9907\/0.8963 (train\/validation) and an IoU score of 0.9816\/0.8132 (train\/validation) on the Decathlon dataset across multiple quantitative metrics. These improvements underscore the efficacy of the proposed dual-attention framework in accurately explaining small, asymmetrical structures such as the hippocampus, while maintaining computational efficiency suitable for clinical deployment.<\/jats:p>","DOI":"10.3390\/jimaging11090311","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T10:50:04Z","timestamp":1757587804000},"page":"311","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Cascaded Spatial and Depth Attention UNet for Hippocampus Segmentation"],"prefix":"10.3390","volume":"11","author":[{"given":"Zi-Zheng","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Computer Science & Engineering, Yuan Ze University, Taoyuan 320, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bich-Thuy","family":"Vu","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Yuan Ze University, Taoyuan 320, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maisam","family":"Abbas","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Yuan Ze University, Taoyuan 320, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran-Zan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Yuan Ze University, Taoyuan 320, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102636","DOI":"10.1016\/j.pneurobio.2024.102636","article-title":"A theory of hippocampal function: New developments","volume":"238","author":"Rolls","year":"2024","journal-title":"Prog. 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