自动分割灵长类追踪数据中的纤维束，提升稀疏束检测与准确性。
Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data
- 基于大块输入的U-Net架构，结合前景感知采样与半监督预训练。
- 稀疏束检测提升20%以上，假阳性率降低40%，可独立分析单张切片。
- 适用于大规模解剖追踪数据自动化处理，助力扩散MRI轨迹优化。
解剖示踪研究对验证和改进扩散磁共振成像（dMRI）轨迹追踪至关重要。然而，大规模分析此类研究数据受限于在组织切片上人工标注纤维束的高劳动成本。现有自动化方法常遗漏稀疏纤维束，或需跨连续切片进行复杂后处理，限制了灵活性与通用性。本文提出一种简化、全自动的猕猴示踪数据纤维束分割框架，基于具有大补丁尺寸的U-Net架构，结合前景感知采样与半监督预训练。该方法有效避免将末端误标为束状结构，稀疏束检测准确率提升超过20%，假发现率（FDR）降低40%，同时支持独立切片分析。新框架将推动解剖追踪数据的大规模自动化分析，生成更多可用于验证和优化dMRI轨迹追踪的高质量真实标签数据。
原文摘要 · Abstract (English)
Anatomic tracer studies are critical for validating and improving diffusion MRI (dMRI) tractography. However, large-scale analysis of data from such studies is hampered by the labor-intensive process of annotating fiber bundles manually on histological slides. Existing automated methods often miss sparse bundles or require complex post-processing across consecutive sections, limiting their flexibility and generalizability. We present a streamlined, fully automated framework for fiber bundle segmentation in macaque tracer data, based on a U-Net architecture with large patch sizes, foreground aware sampling, and semisupervised pre-training. Our approach eliminates common errors such as mislabeling terminals as bundles, improves detection of sparse bundles by over 20% and reduces the False Discovery Rate (FDR) by 40% compared to the state-of-the-art, all while enabling analysis of standalone slices. This new framework will facilitate the automated analysis of anatomic tracing data at a large scale, generating more ground-truth data that can be used to validate and optimize dMRI tractography methods.
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