Automatic Segmentation of the Cisternal Segment of Trigeminal Nerve on MRI Using Deep Learning.

Abstract

Purpose: Accurate segmentation of the cisternal segment of the trigeminal nerve plays a critical role in identifying and treating different trigeminal nerve-related disorders, including trigeminal neuralgia (TN). However, the current manual segmentation process is prone to interobserver variability and consumes a significant amount of time. To overcome this challenge, we propose a deep learning-based approach, U-Net, that automatically segments the cisternal segment of the trigeminal nerve. Methods: To evaluate the efficacy of our proposed approach, the U-Net model was trained and validated on healthy control images and tested in on a separate dataset of TN patients. The methods such as Dice, Jaccard, positive predictive value (PPV), sensitivity (SEN), center-of-mass distance (CMD), and Hausdorff distance were used to assess segmentation performance. Results: Our approach achieved high accuracy in segmenting the cisternal segment of the trigeminal nerve, demonstrating robust performance and comparable results to those obtained by participating radiologists. Conclusion: The proposed deep learning-based approach, U-Net, shows promise in improving the accuracy and efficiency of segmenting the cisternal segment of the trigeminal nerve. To the best of our knowledge, this is the first fully automated segmentation method for the trigeminal nerve in anatomic MRI, and it has the potential to aid in the diagnosis and treatment of various trigeminal nerve-related disorders, such as TN.