Fully Automatic Fine-Grained Grading of Lumbar Intervertebral Disc Degeneration Using Regional Feature Recalibration Network

Accurate fine-grained grading of lumbar intervertebral disc (LIVD) degeneration is essential for the diagnosis and treatment design of high-incidence low back pain. However, the grading accuracy is still challenged by lacking the fine-grained degenerative details, which is mainly due to the existing grading methods are easily dominated by the salient nucleus pulposus regions in LIVD, overlooking the inconspicuous degeneration changes of the surrounding structures. In this study, a novel regional feature recalibration network (RFRecNet) is proposed to achieve accurate and reliable LIVD degeneration grading. Detection transformer (DETR) is first utilized to detect all LIVDs and then input to the proposed RFRecNet for the fine-grained grading. To obtain sufficient features from both the salient nucleus pulposus and the surrounding regions, a regional cube-based feature boosting and suppression (RC-FBS) module is designed to adaptively recalibrate the feature extraction and utilization from the various regions in LIVD, and a feature diversification (FD) module is proposed to capture the complementary semantic information from the multi-scale features for the comprehensive fine-grained degeneration grading. Extensive experiments were conducted on a clinically collected dataset, which consists of 500 MR scans with a total of 10225 LIVDs. An average grading accuracy of 90.5%, specificity of 97.5%, sensitivity of 90.8%, and Cohen's kappa correlation coefficient of 0.876 are obtained, which indicate that the proposed framework is promising to provide doctors with reliable and consistent fine-grained quantitative evaluation results of the LIVD degeneration conditions for the optimal surgical plan design.