Automatic detection, classification, and segmentation of sagittal MR images for diagnosing prolapsed lumbar intervertebral disc.

ABSTRACT: Magnetic resonance (MR) images are commonly used to diagnose prolapsed lumbar intervertebral disc (PLID). However, for a computer-aided diagnostic (CAD) system, distinguishing between pathological abnormalities of PLID in MR images is a challenging and intricate task. Here, we propose a comprehensive model for the automatic detection and cropping of regions of interest (ROI) from sagittal MR images using the YOLOv8 framework to solve this challenge. We also propose weighted average ensemble (WAE) classification and segmentation models for the classification and the segmentation, respectively. YOLOv8 has good detection accuracy for both the lumbar region (mAP50 = 99.50%) and the vertebral disc (mAP50 = 99.40%). The use of ROI approaches enhances the accuracy of individual models. Specifical

SUBMITTER: Sayed MA 

PROVIDER: S-EPMC11697198 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature