Canine Vertebral Column Segmentation with UNet Models: A Positive Approach to Radiographic Image Analysis

Degenerative diseases of the vertebral column, such as spondylosis, are not uncommon in dogs. Detecting these diseases can be challenging and is often overlooked due to lack of effective treatments. The intricacy of medical images poses difficulties in disease identification. Our approach focuses on segmenting the vertebral column from X-ray images of dogs, facilitating targeted disease detection and improving accuracy. Conventional segmentation methods are ineffective for medical images due to presence of occlusions, and pixel classification methods require the laborious creation of masks. By training our UNet models, we successfully achieved precise segmentation of the vertebral column from radiographic images, significantly reducing the time required for generating ground truth masks. Our UNet models effectively enhanced the isolation of the vertebral column from X-ray images, thereby optimizing the accuracy of degenerative disease detection methods for the vertebral column.