Deep learning-assisted framework for automation of lumbar vertebral body segmentation, measurement, and deformity detection in MR images

Abstract

Detecting and accurately quantifying vertebral body dimensions is essential for the assessment of vertebral deformities and fractures. Traditional manual measurement techniques are time-consuming and labor-intensive. This research presents a deep learning-assisted framework for the automatic segmentation, measurement, and deformity detection of lumbar vertebrae in MR images.

The segmentation procedure was implemented using a Mask-RCNN deep convolutional neural network. The framework also included post-processing of segmented vertebral body masks, detection of anatomical landmarks, and calculation of vertebral dimensions. The proposed landmark detection algorithm identified six key vertebral body landmarks (upper and lower anterior, posterior, and middle points) by analyzing pixel distributions in segmented vertebra masks. Using the detected landmarks, the anterior, posterior, and middle heights of each vertebra were calculated through predefined mathematical formulae. These dimensions were used as critical parameters to assess vertebral deformities (wedge and biconcave) by calculating specific height ratios, such as the anterior-posterior ratio and middle-posterior ratio. The dataset included T2-weighted MR images from 200 subjects, divided into subsets of 120 subjects for training, 50 for testing, and 30 for validation.

Mask-RCNN provided a high median Dice coefficient of 0.95 for vertebral body segmentation on a test subset. Median absolute errors of anterior, middle, and posterior heights measurements were 0.783, 0.856, and 0.785 mm, respectively. The algorithm allowed measuring wedge and biconcave vertebral deformities with median errors of 3.3 % and 4.4 %. The proposed framework is simpler and more universal than the existing methods and significantly automates the precise assessment of lumbar vertebrae deformities.