LEM-UNet: an edge-guided network for 3D multimodal images segmentation in focal cortical dysplasia
The results demonstrate the importance of focusing on lesion edge in the FCD segmentation task. The integration of the Laplacian pyramid enhances the mode's ability to capture lesions with blurred edge and subtle features. LEM-UNet exhibits significant advantages over current FCD segmentation algorithms. The source code and pre trained model weights are available at https://github.com/simplify403/LEM-UNet.
Authors
- M. Zhang (ORCID: https://orcid.org/0009-0002-1715-1289)
- Lixin Cai (ORCID: https://orcid.org/0000-0001-8231-6428)
- Qiunan Li
- Guixia Kang (ORCID: https://orcid.org/0000-0002-4039-4505)
- Xiaotong Yuan (ORCID: https://orcid.org/0000-0001-8382-2138)
- Hao Yu (ORCID: https://orcid.org/0000-0002-5164-4792)
Institutions
- Beijing University of Posts and Telecommunications (CN)
- Peking University (CN)
- Peking University First Hospital (CN)
Publication Details
- Journal
- Frontiers in Neuroscience
- Published
- 2025-10-08
- DOI
- https://doi.org/10.3389/fnins.2025.1634606
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China