提出多层级嵌入框架，提升脑白质纤维束分割精度
TractoEmbed: Modular Multi-level Embedding framework for white matter tract segmentation
- 分层编码纤维束、簇和局部块的局部特征
- 在多个数据集和年龄组上超越现有最优方法
- 模块化设计便于未来融合新嵌入方法
白质纤维束分割对研究大脑结构连接与神经外科规划至关重要。然而，由于主要与次要纤维束间类别不平衡、结构相似性、个体差异以及左右半球对称纤维束等问题，分割仍具挑战。为此，我们提出 TractoEmbed，一种模块化多层级嵌入框架，通过各编码器学习任务来编码局部表征。本文引入一种新的分层纤维束数据表示方法，在每一层级（单条纤维束、簇、局部块）尽可能保留空间信息。实验表明，TractoEmbed 在不同数据集及跨年龄组的白质纤维束分割任务中均优于现有最先进方法。该模块化框架可直接支持未来研究中新增嵌入的集成。
原文摘要 · Abstract (English)
White matter tract segmentation is crucial for studying brain structural connectivity and neurosurgical planning. However, segmentation remains challenging due to issues like class imbalance between major and minor tracts, structural similarity, subject variability, symmetric streamlines between hemispheres etc. To address these challenges, we propose TractoEmbed, a modular multi-level embedding framework, that encodes localized representations through learning tasks in respective encoders. In this paper, TractoEmbed introduces a novel hierarchical streamline data representation that captures maximum spatial information at each level i.e. individual streamlines, clusters, and patches. Experiments show that TractoEmbed outperforms state-of-the-art methods in white matter tract segmentation across different datasets, and spanning various age groups. The modular framework directly allows the integration of additional embeddings in future works.
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