Sundaresan Vaanathi, Lehman Julia F, Maffei Chiara, Haber Suzanne N, Yendiki Anastasia
Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, Karnataka 560012, India.
Department of Pharmacology and Physiology, University of Rochester School of Medicine, Rochester, NY, United States.
bioRxiv. 2025 Feb 1:2023.09.30.560310. doi: 10.1101/2023.09.30.560310.
Anatomic tracing is the gold standard tool for delineating brain connections and for validating more recently developed imaging approaches such as diffusion MRI tractography. A key step in the analysis of data from tracer experiments is the careful, manual charting of fiber trajectories on histological sections. This is a very time-consuming process, which limits the amount of annotated tracer data that are available for validation studies. Thus, there is a need to accelerate this process by developing a method for computer-assisted segmentation. Such a method must be robust to the common artifacts in tracer data, including variations in the intensity of stained axons and background, as well as spatial distortions introduced by sectioning and mounting the tissue. The method should also achieve satisfactory performance using limited manually charted data for training. Here we propose the first deep-learning method, with a self-supervised loss function, for segmentation of fiber bundles on histological sections from macaque brains that have received tracer injections. We address the limited availability of manual labels with a semi-supervised training technique that takes advantage of unlabeled data to improve performance. We also introduce anatomic and across-section continuity constraints to improve accuracy. We show that our method can be trained on manually charted sections from a single case and segment unseen sections from different cases, with a true positive rate of ~0.80. We further demonstrate the utility of our method by quantifying the density of fiber bundles as they travel through different white-matter pathways. We show that fiber bundles originating in the same injection site have different levels of density when they travel through different pathways, a finding that can have implications for microstructure-informed tractography methods. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing.
解剖追踪是描绘脑连接以及验证诸如扩散磁共振成像纤维束成像等最新开发的成像方法的金标准工具。示踪剂实验数据分析中的关键步骤是在组织学切片上仔细地手动绘制纤维轨迹。这是一个非常耗时的过程，限制了可用于验证研究的带注释示踪剂数据的数量。因此，需要通过开发一种计算机辅助分割方法来加速这一过程。这样的方法必须对示踪剂数据中的常见伪影具有鲁棒性，包括染色轴突和背景强度的变化，以及切片和固定组织引入的空间扭曲。该方法还应使用有限的手动绘制数据进行训练，以达到令人满意的性能。在这里，我们提出了第一种具有自监督损失函数的深度学习方法，用于对接受示踪剂注射的猕猴脑的组织学切片上的纤维束进行分割。我们使用一种半监督训练技术来解决手动标签可用性有限的问题，该技术利用未标记数据来提高性能。我们还引入了解剖学和跨切片连续性约束来提高准确性。我们表明，我们的方法可以在单个病例的手动绘制切片上进行训练，并分割来自不同病例的未见过的切片，真阳性率约为0.80。我们通过量化纤维束在不同白质通路中穿行时的密度，进一步证明了我们方法的实用性。我们表明，起源于同一注射部位的纤维束在穿过不同通路时具有不同的密度水平，这一发现可能对基于微观结构的纤维束成像方法有影响。我们方法的代码可在https://github.com/v-sundaresan/fiberbundle_seg_tracing获取。
