Bibliographic Details
| Title: |
MTMU: Multi-domain Transformation based Mamba-UNet designed for unruptured intracranial aneurysm segmentation |
| Authors: |
Bing Li, Nian Liu, Jianbin Bai, Jianfeng Xu, Yi Tang, Yan Liu |
| Source: |
BMC Medical Imaging, Vol 25, Iss 1, Pp 1-16 (2025) |
| Publisher Information: |
BMC, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Medical technology |
| Subject Terms: |
Intracranial aneurysm segmentation, Mamba unit, Fourier transform, Geometry constrain, Medical technology, R855-855.5 |
| More Details: |
Abstract The management of Unruptured Intracranial aneurysm (UIA) depends on the shape parameters assessment of lesions, which requires target segmentation. However, the segmentation of UIA is a challenging task due to the small volume of the lesions and the indistinct boundary between the lesion and the parent arteries. To relieve these issues, this article proposes a multi-domain transformation-based Mamba-UNet (MTMU) for UIA segmentation. The model employs a U-shaped segmentation architecture, equipped with the feature encoder consisting of a set of Mamba and Flip (MF) blocks. It endows the model with the capability of long-range dependency perceiving while balancing computational cost. Fourier Transform (FT) based connection allows for the enhancement of edge information in feature maps, thereby mitigating the difficulties in feature extraction caused by the small size of the target and the limited number of foreground pixels. Additionally, a sub task providing target geometry constrain (GC) is utilized to constrain the model training, aiming at splitting aneurysm dome from its parent artery accurately. Extensive experiments have been conducted to demonstrate the superior performance of the proposed method compared to other competitive medical segmentation methods. The results prove that the proposed method have great clinical application prospects. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1471-2342 |
| Relation: |
https://doaj.org/toc/1471-2342 |
| DOI: |
10.1186/s12880-025-01611-6 |
| Access URL: |
https://doaj.org/article/f407b54399834a0bb6d9f4ea71444a1f |
| Accession Number: |
edsdoj.f407b54399834a0bb6d9f4ea71444a1f |
| Database: |
Directory of Open Access Journals |
