Buyukcelik Ozge N, Lapierre-Landry Maryse, Kolluru Chaitanya, Upadhye Aniruddha R, Marshall Daniel P, Pelot Nicole A, Ludwig Kip A, Gustafson Kenneth J, Wilson David L, Jenkins Michael W, Shoffstall Andrew J
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States.
Advanced Platform Technologies Center, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States.
Front Neurosci. 2023 May 10;17:1169187. doi: 10.3389/fnins.2023.1169187. eCollection 2023.
MicroCT of the three-dimensional fascicular organization of the human vagus nerve provides essential data to inform basic anatomy as well as the development and optimization of neuromodulation therapies. To process the images into usable formats for subsequent analysis and computational modeling, the fascicles must be segmented. Prior segmentations were completed manually due to the complex nature of the images, including variable contrast between tissue types and staining artifacts.
Here, we developed a U-Net convolutional neural network (CNN) to automate segmentation of fascicles in microCT of human vagus nerve.
The U-Net segmentation of ~500 images spanning one cervical vagus nerve was completed in 24 s, versus ~40 h for manual segmentation, i.e., nearly four orders of magnitude faster. The automated segmentations had a Dice coefficient of 0.87, a measure of pixel-wise accuracy, thus suggesting a rapid and accurate segmentation. While Dice coefficients are a commonly used metric to assess segmentation performance, we also adapted a metric to assess fascicle-wise detection accuracy, which showed that our network accurately detects the majority of fascicles, but may under-detect smaller fascicles.
This network and the associated performance metrics set a benchmark, using a standard U-Net CNN, for the application of deep-learning algorithms to segment fascicles from microCT images. The process may be further optimized by refining tissue staining methods, modifying network architecture, and expanding the ground-truth training data. The resulting three-dimensional segmentations of the human vagus nerve will provide unprecedented accuracy to define nerve morphology in computational models for the analysis and design of neuromodulation therapies.
对人类迷走神经的三维束状结构进行显微CT扫描可为基础解剖学以及神经调节疗法的开发与优化提供重要数据。为了将图像处理成可用于后续分析和计算建模的格式，必须对神经束进行分割。由于图像的复杂性，包括组织类型之间的对比度变化和染色伪影，之前的分割是手动完成的。
在此，我们开发了一种U-Net卷积神经网络（CNN），以自动分割人类迷走神经显微CT图像中的神经束。
跨越一条颈迷走神经的约500张图像的U-Net分割在24秒内完成，而手动分割需要约40小时，即快了近四个数量级。自动分割的骰子系数为0.87，这是一种逐像素准确性的度量，表明分割快速且准确。虽然骰子系数是评估分割性能的常用指标，但我们还采用了一种指标来评估神经束级别的检测准确性，结果表明我们的网络能够准确检测出大多数神经束，但可能会漏检较小的神经束。
该网络及相关性能指标使用标准的U-Net CNN为深度学习算法在从显微CT图像中分割神经束的应用设定了基准。通过改进组织染色方法、修改网络架构和扩展真实训练数据，该过程可能会进一步优化。由此得到的人类迷走神经三维分割将为神经调节疗法分析和设计的计算模型中的神经形态定义提供前所未有的准确性。
