使用几何深度学习对解剖学上不合理的纤维进行追踪图过滤
Tractogram filtering of anatomically non-plausible fibers with geometric deep learning
摘要：
纤维束描记图是大脑白质纤维的虚拟表示。他们主要感兴趣的任务，如术前规划，神经可塑性或大脑疾病的调查。每个纤维束图都由数百万条编码为3D折线的纤维组成。不幸的是，这些纤维中的大部分在解剖学上是不合理的，并且可以被认为是跟踪算法的伪影。用于纤维束描记图滤波的常见方法基于信号重建，这是一种原则性方法，但无法考虑脑解剖学知识。在这项工作中，我们解决了问题的跟踪图过滤作为监督学习问题，利用地面实况注释获得最近的启发式方法，标签纤维作为解剖学上合理或不合理的根据完善的解剖学特性。直观的想法是将纤维建模为点云，目标是研究几何深度学习模型是否以及如何捕获其解剖特性。我们的贡献是动态边缘卷积模型的扩展，该模型利用纤维中点的顺序关系，并以高精度区分似然/非似然纤维。
英文摘要：
Tractograms are virtual representations of the white matter fibers of the brain. They are of primary interest for tasks like presurgical planning, and investigation of neuroplasticity or brain disorders. Each tractogram is composed of millions of fibers encoded as 3D polylines. Unfortunately, a large portion of those fibers are not anatomically plausible and can be considered artifacts of the tracking algorithms. Common methods for tractogram filtering are based on signal reconstruction, a principled approach, but unable to consider the knowledge of brain anatomy. In this work, we address the problem of tractogram filtering as a supervised learning problem by exploiting the ground truth annotations obtained with a recent heuristic method, which labels fibers as either anatomically plausible or non-plausible according to well-established anatomical properties. The intuitive idea is to model a fiber as a point cloud and the goal is to investigate whether and how a geometric deep learning model might capture its anatomical properties. Our contribution is an extension of the Dynamic Edge Convolution model that exploits the sequential relations of points in a fiber and discriminates with high accuracy plausible/non-plausible fibers.
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