UNet-based MRI spinal vertebral body center extraction

The spine is a vital part of the body's skeletal structure, connecting the entire skeletal framework and providing stability to the torso, head, and neck. When the spine is diseased, the spatial morphology of the spine will change accordingly. Therefore, spinal spatial pattern analysis is a crucial method to assess the health status of the spine. In the spatial morphology description of the spine, the line connecting each vertebra's center is generally used as the spatial morphology of the spine. Hence, extracting the center of the spine's vertebra becomes crucial. Currently, the vertebral centers in three-dimensional (3D) space are mainly extracted using the extraction of vertebral centers in a single image on a two-dimensional (2D) MRI or constraints on vertebral shapes based on prior knowledge. Therefore, this paper proposes a more stable and objective vertebral center extraction method with strong subjective factors and poor extraction accuracy for the current whole spine vertebral center extraction; it also analyzes and demonstrates its accuracy and stability. The specific method follows: firstly, the spine MR images in the sagittal plane are trained with the neural network by binary classification; after that, segmentation is carried out by using a neural network, and then each spine MRI obtained by segmentation is used to perform the pixel level's output of probability maps based on each vertebral body; finally, the 3D coordinates of all the pixel points of the whole spine MRI sequences are extracted and superimposed to form a body, which is combined with the probability maps outputted before to obtain the center of the entire spine. The experimental results showed that the average measurement error of this method was 1.48 mm for 23 vertebrae, and the measurement results remained unchanged after repeating the measurement three times, which was better than other measurement methods in terms of accuracy and stability.