High-throughput segmentation of unmyelinated axons by deep learning.
High-throughput segmentation of unmyelinated axons by deep learning.
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DOI：
10.1038/s41598-022-04854-3
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发表时间：
2022-01-24
影响因子：
4.6
通讯作者：
Dundar MM
中科院分区：
文献类型：
--
作者：
Plebani E;Biscola NP;Havton LA;Rajwa B;Shemonti AS;Jaffey D;Powley T;Keast JR;Lu KH;Dundar MM
健康和疾病表型中连接的轴突特征令人惊讶地不完整和有偏见，因为神经系统中最常见的纤维类型无髓轴突在很大程度上被忽视了，因为随着轴突数量的增加，它们的定量评估很快变得难以管理。在这里，我们介绍了用于无髓纤维自动分割的高通量处理流水线的第一个原型。我们的团队使用了大鼠迷走神经和盆神经的透射电子显微镜图像。这些图像中的所有无髓鞘轴突都被单独注释并用作标记数据，以训练和验证深度实例分割网络。研究了不同训练策略对网络整体分割精度的影响。我们广泛地验证了分割算法作为独立分割工具以及在循环专家混合分割设置中的有效性，并取得了初步的、尽管非常令人鼓舞的结果。在独立模式下，我们的算法在各种测试图像上的实例级评分在0.7到0.9之间，在混合模式下，专家标注的工作量减少了80%。我们希望这一新的高通量分段管道将能够快速准确地描述规模的无髓纤维，并有助于显著促进我们对周围和中枢神经系统中的连接的理解。
Axonal characterizations of connectomes in healthy and disease phenotypes are surprisingly incomplete and biased because unmyelinated axons, the most prevalent type of fibers in the nervous system, have largely been ignored as their quantitative assessment quickly becomes unmanageable as the number of axons increases. Herein, we introduce the first prototype of a high-throughput processing pipeline for automated segmentation of unmyelinated fibers. Our team has used transmission electron microscopy images of vagus and pelvic nerves in rats. All unmyelinated axons in these images are individually annotated and used as labeled data to train and validate a deep instance segmentation network. We investigate the effect of different training strategies on the overall segmentation accuracy of the network. We extensively validate the segmentation algorithm as a stand-alone segmentation tool as well as in an expert-in-the-loop hybrid segmentation setting with preliminary, albeit remarkably encouraging results. Our algorithm achieves an instance-level score of between 0.7 and 0.9 on various test images in the stand-alone mode and reduces expert annotation labor by 80% in the hybrid setting. We hope that this new high-throughput segmentation pipeline will enable quick and accurate characterization of unmyelinated fibers at scale and become instrumental in significantly advancing our understanding of connectomes in both the peripheral and the central nervous systems.
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