Deep-learning segmentation of fascicles from microCT of the human vagus nerve

Ozge N Buyukcelik; Maryse Lapierre-Landry; Chaitanya Kolluru; Aniruddha R Upadhye; Daniel P Marshall; Nicole A Pelot; Kip A Ludwig; Kenneth J Gustafson; David L Wilson; Michael W Jenkins; Andrew J Shoffstall

doi:10.3389/fnins.2023.1169187

Deep-learning segmentation of fascicles from microCT of the human vagus nerve

Front Neurosci. 2023 May 10:17:1169187. doi: 10.3389/fnins.2023.1169187. eCollection 2023.

Authors

Ozge N Buyukcelik^{1

2}, Maryse Lapierre-Landry¹, Chaitanya Kolluru¹, Aniruddha R Upadhye^{1

2}, Daniel P Marshall³, Nicole A Pelot³, Kip A Ludwig^{4

5

6}, Kenneth J Gustafson^{1

7}, David L Wilson¹, Michael W Jenkins^{1

8}, Andrew J Shoffstall^{1

2}

Affiliations

¹ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States.
² Advanced Platform Technologies Center, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States.
³ Department of Biomedical Engineering, Duke University, Durham, NC, United States.
⁴ Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI, United States.
⁵ Department of Neurological Surgery, University of Wisconsin Madison, Madison, WI, United States.
⁶ Wisconsin Institute for Translational Neuroengineering, Madison, WI, United States.
⁷ Functional Electrical Stimulation Center, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States.
⁸ Department of Pediatrics, School of Medicine, Case Western Reserve University, Cleveland, OH, United States.

Abstract

Introduction: 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.

Methods: Here, we developed a U-Net convolutional neural network (CNN) to automate segmentation of fascicles in microCT of human vagus nerve.

Results: 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.

Discussion: 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.

Keywords: autonomic nervous system; deep-learning segmentation of vagus nerve; image processing; microCT; neural network; segmentation; vagus nerve.