arXiv 2502.206372025-03-06cs.CV

TractCloud-FOV：基于深度学习的扩散MRI中不完整视野下的鲁棒纤维束追踪分割

TractCloud-FOV: Deep Learning-based Robust Tractography Parcellation in Diffusion MRI with Incomplete Field of View

Harvard Medical School（哈佛医学院）；Harvard University（哈佛大学）；The University of Sydney（悉尼大学）；Massachusetts General Hospital（麻省总医院）；University of Electronic Science and Technology of China（电子科技大学）；Harvard-MIT Health Sciences and Technology（哈佛-麻省理工健康科学与技术部）

Yuqian Chen, Leo Zekelman, Yui Lo, Suheyla Cetin-Karayumak, Tengfei Xue, Yogesh Rathi, Nikos Makris, Fan Zhang, Weidong Cai, Lauren J. O'Donnell

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AI总结：

针对扩散MRI中不完整视野（FOV）导致纤维束截断的问题，提出深度学习框架TractCloud-FOV，通过FOV-Cut Augmentation数据增强策略模拟真实截断场景，在分类精度、泛化能力和计算效率上显著优于现有方法。

摘要：

纤维束追踪分割将扩散MRI重建的纤维流线分类为解剖学定义的纤维束，以用于临床和研究应用。然而，临床扫描通常具有不完整的视野（FOV），导致大脑区域仅被部分成像，从而产生部分或截断的纤维束。为了应对这一挑战，我们引入了TractCloud-FOV，这是一种在不完整FOV条件下能够鲁棒地分割纤维束追踪的深度学习框架。我们提出了一种新颖的训练策略，即FOV-Cut Augmentation（FOV-CA），在其中我们合成性地切割纤维束图，以模拟一系列真实世界中的下方FOV截断场景。这种数据增强方法用真实的截断纤维流线丰富了训练集，使模型能够实现卓越的泛化能力。我们在合成切割的纤维束追踪和两个具有不完整FOV的真实数据集上评估了所提出的TractCloud-FOV。在纤维流线分类精度、泛化能力、纤维束解剖描绘和计算效率方面，TractCloud-FOV在所有测试数据集上均显著优于几种最先进的方法。总体而言，TractCloud-FOV在不完整FOV的扩散MRI中实现了高效且一致的纤维束追踪分割。

英文摘要：

Tractography parcellation classifies streamlines reconstructed from diffusion MRI into anatomically defined fiber tracts for clinical and research applications. However, clinical scans often have incomplete fields of view (FOV) where brain regions are partially imaged, leading to partial or truncated fiber tracts. To address this challenge, we introduce TractCloud-FOV, a deep learning framework that robustly parcellates tractography under conditions of incomplete FOV. We propose a novel training strategy, FOV-Cut Augmentation (FOV-CA), in which we synthetically cut tractograms to simulate a spectrum of real-world inferior FOV cutoff scenarios. This data augmentation approach enriches the training set with realistic truncated streamlines, enabling the model to achieve superior generalization. We evaluate the proposed TractCloud-FOV on both synthetically cut tractography and two real-life datasets with incomplete FOV. TractCloud-FOV significantly outperforms several state-of-the-art methods on all testing datasets in terms of streamline classification accuracy, generalization ability, tract anatomical depiction, and computational efficiency. Overall, TractCloud-FOV achieves efficient and consistent tractography parcellation in diffusion MRI with incomplete FOV.

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