用解剖结构信息提升脑部MRI分割精度
CA-Diff: Collaborative Anatomy Diffusion for Brain Tissue Segmentation
- 引入距离场作为解剖条件，提供全局空间上下文
- 在多模态数据上达到当前最优分割性能
- 适合医学图像分析与深度学习研究者参考
从MRI中分割脑结构对评估脑形态至关重要，但现有基于CNN和Transformer的方法在精确划分复杂结构方面表现不佳。尽管扩散模型在图像分割中展现潜力，但直接应用于脑部MRI时因忽略解剖信息而效果受限。为此，我们提出协同解剖扩散框架（CA-Diff），通过引入距离场作为辅助解剖条件，提供全局空间上下文，并设计协同扩散过程建模其与解剖结构的联合分布，以有效利用解剖特征。此外，引入一致性损失优化距离场与解剖结构间关系，并设计时间自适应通道注意力模块增强U-Net特征融合。大量实验表明，CA-Diff超越现有最先进方法。
原文摘要 · Abstract (English)
Segmentation of brain structures from MRI is crucial for evaluating brain morphology, yet existing CNN and transformer-based methods struggle to delineate complex structures accurately. While current diffusion models have shown promise in image segmentation, they are inadequate when applied directly to brain MRI due to neglecting anatomical information. To address this, we propose Collaborative Anatomy Diffusion (CA-Diff), a framework integrating spatial anatomical features to enhance segmentation accuracy of the diffusion model. Specifically, we introduce distance field as an auxiliary anatomical condition to provide global spatial context, alongside a collaborative diffusion process to model its joint distribution with anatomical structures, enabling effective utilization of anatomical features for segmentation. Furthermore, we introduce a consistency loss to refine relationships between the distance field and anatomical structures and design a time adapted channel attention module to enhance the U-Net feature fusion procedure. Extensive experiments show that CA-Diff outperforms state-of-the-art (SOTA) methods.
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