Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI

Globally, as a major public health problem, low back pain has been the leading cause of disability worldwide for the past 30 years. Here, the authors propose a segmentation network and a quantitative method lumbar intervertebral disc degeneration assessment. To help doctors and patients evaluate lumbar intervertebral disc degeneration (IVDD) accurately and efficiently, we propose a segmentation network and a quantitation method for IVDD from T2MRI. A semantic segmentation network (BianqueNet) composed of three innovative modules achieves high-precision segmentation of IVDD-related regions. A quantitative method is used to calculate the signal intensity and geometric features of IVDD. Manual measurements have excellent agreement with automatic calculations, but the latter have better repeatability and efficiency. We investigate the relationship between IVDD parameters and demographic information (age, gender, position and IVDD grade) in a large population. Considering these parameters present strong correlation with IVDD grade, we establish a quantitative criterion for IVDD. This fully automated quantitation system for IVDD may provide more precise information for clinical practice, clinical trials, and mechanism investigation. It also would increase the number of patients that can be monitored.

Automated summary

Low back pain has been a leading cause of disability worldwide. This study proposes a segmentation network and a quantitative method for evaluating lumbar intervertebral disc degeneration. The technique can accurately assess the severity of low back pain and provide more precise information.