A Dual-Task Semi-Supervised Neural Network Based on Skew Normal Mixture Model for Brain MR Image Segmentation
Year: 2024
Author: Shengyang Liao, Yunjie Chen
Journal of Information and Computing Science, Vol. 19 (2024), Iss. 2 : pp. 131–154
Abstract
Accurate segmentation of brain magnetic resonance (MR) images is critical in brain disease research and treatment. While deep learning methods have advanced image segmentation by extracting hierarchical features, they typically require large labeled datasets for precise results. Acquiring annotated medical data remains challenging due to the need for specialized expertise and privacy restrictions. To address this, we propose a semi-supervised model combining dual tasks: segmentation and boundary feature regression. For class imbalance in segmentation, the network employs focal loss to extract common features from annotated data. To handle asymmetric data distributions, a skew Normal Mixture-based Level set loss guides the network to learn individual image characteristics, enhancing class distribution fitting. This dual-feature integration enables strong performance on limited datasets. In regression, Level set signed distance functions focus the model on boundary information, mitigating partial volume effects on focal loss. Experiments on IBSR and MRBrainS18 datasets demonstrate our method’s advantages over current state-of-the-art approaches.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/JICS-2024-008
Journal of Information and Computing Science, Vol. 19 (2024), Iss. 2 : pp. 131–154
Published online: 2024-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 24
Keywords: Brain MR image segmentation Semi-supervised Learning Skew Normal Mixture model Level set functional Dual task.