示踪剂组织学中纤维束分割的拓扑感知训练与空间诊断

中文摘要 AI 辅助

解剖示踪剂研究揭示轴突束如何从注射位点投射、分支为更小的轴突组，并在脑内走行至目标区域。此类研究的组织学数据为验证扩散MRI纤维束成像提供了解剖参考信息。然而，组织学数据的手动标注工作量极大，尽管已提出自动分割方法，但这些方法主要依赖BCE、Dice等像素重叠损失函数，尚未针对该任务研究拓扑感知损失函数。我们使用冻结的DINOv3骨干网络，在猕猴示踪剂组织学数据上比较BCE-Dice、clDice、Betti匹配和Topograph四种方法用于纤维束分割。据我们所知，这是首次探索基础模型特征在该任务中的应用。BCE-Dice取得最高Dice值，clDice取得最高束召回率但掩码重叠度较差，Topograph的Dice值与BCE-Dice相近，具有最低的β₀误差，且假阳性数量少于BCE-Dice和Betti匹配。纤维束分割方法通常采用宽松规则评估：只要预测结果与标注束存在任何重叠，即视为检测到该束。我们表明该规则无法捕捉过分割问题，且空切片被分配完美召回率会夸大每切片的TPR。为量化该问题，我们提出Excess32，即一种空间诊断指标，用于测量预测像素超出标注束周围32像素容差带的数量。验证实验中，Betti-Topograph的组合使稀疏束TPR从0.818提升至0.933，但使FDR从0.296升至0.509、Excess32从0.108升至0.466、面积比从0.94升至3.34。这些结果表明，仅用检测指标不足以表征分割质量。

英文摘要

Anatomic tracer studies reveal how axon bundles project from an injection site, branch into smaller groups of axons, and course through the brain to reach their destinations. Histological data from such studies provide anatomical reference information for validating diffusion MRI tractography. However, manual annotation of the histological data is very labor-intensive, and although automated segmentation methods have been proposed, they rely mainly on pixel-overlap losses such as BCE and Dice; topology-aware loss functions have not been studied for this task. We compare BCE-Dice, clDice, Betti matching, and Topograph for fiber bundle segmentation in macaque tracer histology using a frozen DINOv3 backbone. To our knowledge, this is the first exploration of foundation-model features for this task. BCE-Dice achieved the highest Dice, while clDice achieved the highest bundle recall but poor mask overlap. Topograph had similar Dice to BCE-Dice, the lowest $β_0$ error, and fewer false positives than BCE-Dice and Betti matching. Fiber bundle segmentation methods are typically evaluated with a permissive rule that counts a bundle as detected given any overlap with the prediction. We show this rule does not capture oversegmentation, and that per-section TPR can be inflated by empty sections assigned perfect recall. To quantify this, we introduce Excess32, a spatial diagnostic measuring predicted pixels outside a 32-pixel tolerance band around annotated bundles. In validation, a Betti-Topograph union raises sparse-bundle TPR from 0.818 to 0.933, but worsens FDR from 0.296 to 0.509, Excess32 from 0.108 to 0.466, and area ratio from 0.94 to 3.34. These results show detection metrics alone are insufficient to characterize segmentation quality.

发表机构

• Universidad Nacional Mayor de San Marcos（圣马科斯国立大学）
• Athinoula A. Martinos Center for Biomedical Imaging（阿西诺拉·A.马蒂诺斯生物医学成像中心）
• Massachusetts General Hospital（麻省总医院）
• Harvard Medical School（哈佛医学院）
• University of Rochester School of Medicine（罗切斯特大学医学院）
• McLean Hospital（麦克莱恩医院）

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