Prediction of the Topography of the Corticospinal Tract on T1-Weighted MR Images Using Deep-Learning-Based Segmentation

Introduction: Tractography is an invaluable tool in the planning of tumor surgery in the vicinity of functionally eloquent areas of the brain as well as in the research of normal development or of various diseases. The aim of our study was to compare the performance of a deep-learning-based image segmentation for the prediction of the topography of white matter tracts on T1-weighted MR images to the performance of a manual segmentation. Methods: T1-weighted MR images of 190 healthy subjects from 6 different datasets were utilized in this study. Using deterministic diffusion tensor imaging, we first reconstructed the corticospinal tract on both sides. After training a segmentation model on 90 subjects of the PIOP2 dataset using the nnU-Net in a cloud-based environment with graphical processing unit (Google Colab), we evaluated its performance using 100 subjects from 6 different datasets. Results: Our algorithm created a segmentation model that predicted the topography of the corticospinal pathway on T1-weighted images in healthy subjects. The average dice score was 0.5479 (0.3513-0.7184) on the validation dataset. Conclusions: Deep-learning-based segmentation could be applicable in the future to predict the location of white matter pathways in T1-weighted scans.

Automated summary

This study aimed to compare the performance of a deep-learning-based image segmentation with manual segmentation for predicting the topography of white matter tracts on T1-weighted MR images. After reconstructing the corticospinal tract using deterministic diffusion tensor imaging, a segmentation model was trained and evaluated. The results showed that the algorithm performed well in predicting the topography of the corticospinal pathway on T1-weighted images in healthy subjects.