MG-Net: A fetal brain tissue segmentation method based on multiscale feature fusion and graph convolution attention mechanisms
In this paper, we propose a model for three-dimensional fetal brain tissue segmentation based on multi-scale feature fusion and graph convolution attention mechanism, and conduct experimental evaluation on the FeTA 2021 and FeTA 2022 datasets. Understanding the boundaries of fetal brain tissue is crucial for doctors' diagnosis, so the proposed model is expected to improve the speed and accuracy of doctors' diagnoses.
Authors
- Dong Liang (ORCID: https://orcid.org/0000-0001-6257-0875)
- Keying Qi (ORCID: https://orcid.org/0009-0007-7227-2450)
- Bing Zhang (ORCID: https://orcid.org/0000-0001-7311-9844)
- Chenchen Yan
- Xiaojing Long (ORCID: https://orcid.org/0000-0003-4455-7831)
- Donghao Niu
Institutions
- Chinese Academy of Sciences (CN)
- Nanjing Drum Tower Hospital (CN)
- Shenzhen Institutes of Advanced Technology (CN)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2024-10-05
- DOI
- https://doi.org/10.1016/j.cmpb.2024.108451
- Citations
- 2
- Primary Topic
- Domain Adaptation and Few-Shot Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China