论文标题
限制自我监督的方法，随时间结合用于解剖学数据的纤维束检测
Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data
论文作者
论文摘要
解剖跟踪数据提供了有关脑电路的详细信息，这些信息对于解决扩散MRI拖拉术中的某些常见误差必不可少。然而，由于截断，噪声和伪影的存在以及强度/对比度变化，因此在跟踪数据上对纤维束的自动检测具有挑战性。在这项工作中，我们提出了一种具有自我监督的损失函数的深度学习方法，该方法将基于解剖学的损失函数构成了基于解剖学的约束，以准确地分割猕猴的示踪剂截面上的纤维束。同样，鉴于手动标签的可用性有限，我们使用半监督的训练技术有效地使用未标记的数据来改善性能，并具有进一步降低误报的位置限制。对不同猕猴的看不到部分的方法的评估可产生令人鼓舞的结果，而实际的正速率约为0.90。我们方法的代码可从https://github.com/v-sundaresan/fiberbundle_seg_tracing获得。
Anatomic tracing data provides detailed information on brain circuitry essential for addressing some of the common errors in diffusion MRI tractography. However, automated detection of fiber bundles on tracing data is challenging due to sectioning distortions, presence of noise and artifacts and intensity/contrast variations. In this work, we propose a deep learning method with a self-supervised loss function that takes anatomy-based constraints into account for accurate segmentation of fiber bundles on the tracer sections from macaque brains. Also, given the limited availability of manual labels, we use a semi-supervised training technique for efficiently using unlabeled data to improve the performance, and location constraints for further reduction of false positives. Evaluation of our method on unseen sections from a different macaque yields promising results with a true positive rate of ~0.90. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing.
