用双视角互学习提升低场MRI脑图分割精度
Bilateral Hippocampi Segmentation in Low Field MRIs Using Mutual Feature Learning via Dual-Views
- 双视角结构通过高频掩码实现特征互补学习
- 在低场MRI上达到可靠分割效果，适合资源匮乏地区
- 方法可推广至儿童神经发育障碍早期筛查
大脑MRI中海马体的精确分割对研究认知与记忆功能及诊断神经发育障碍至关重要。尽管高场磁共振成像提供详细图像，但低场磁共振更易获取且成本更低，无需儿童镇静，但常因图像质量较差而受限。本文提出一种新型深度学习方法，用于低场MRI中双侧海马体的自动分割。借鉴先前工作Co-BioNet，所提模型采用双视角结构，通过高频掩码实现互学习，利用不同视角的互补信息提升分割精度。大量实验证明，该方法可在资源有限环境下实现可靠的海马体分析。代码已公开于：https://github.com/himashi92/LoFiHippSeg。
原文摘要 · Abstract (English)
Accurate hippocampus segmentation in brain MRI is critical for studying cognitive and memory functions and diagnosing neurodevelopmental disorders. While high-field MRIs provide detailed imaging, low-field MRIs are more accessible and cost-effective, which eliminates the need for sedation in children, though they often suffer from lower image quality. In this paper, we present a novel deep-learning approach for the automatic segmentation of bilateral hippocampi in low-field MRIs. Extending recent advancements in infant brain segmentation to underserved communities through the use of low-field MRIs ensures broader access to essential diagnostic tools, thereby supporting better healthcare outcomes for all children. Inspired by our previous work, Co-BioNet, the proposed model employs a dual-view structure to enable mutual feature learning via high-frequency masking, enhancing segmentation accuracy by leveraging complementary information from different perspectives. Extensive experiments demonstrate that our method provides reliable segmentation outcomes for hippocampal analysis in low-resource settings. The code is publicly available at: https://github.com/himashi92/LoFiHippSeg.
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