Deep learning model for segmentation of trabecular tissue on CT data of the lumbar spine

Abstract

This paper focuses on training a deep learning model for vertebral body segmentation of the lumbar spine. The nnU-Net model was trained and tested on a publicly available dataset LumVBCanSeg consisting of 185 lumbar CT scans. Dice coefficient was used to evaluate the accuracy of the trained model. The mean Dice coefficient of the testing dataset was 0.949 with a standard deviation of 0.103. The model was also tested on clinical data containing various abnormalities, such as lytic lesions in multiple myeloma patients and metallic implants. Results were evaluated visually. While the model showed high accuracy on the testing dataset, the results on scans with anomalies showed a decline in accuracy.