SpineGEM: A Hybrid-Supervised Model Generation Strategy Enabling Accurate Spine Disease Classification with a Small Training Dataset

Most deep-learning based magnetic resonance image (MRI) analysis methods require numerous amounts of labelling work manually done by specialists, which is laborious and time-consuming. In this paper, we aim to develop a hybrid-supervised model generation strategy, called SpineGEM, which can economically generate a high-performing deep learning model for the classification of multiple pathologies of lumbar degeneration disease (LDD). A unique self-supervised learning process is adopted to generate a pre-trained model, with no pathology labels or human interventions required. The anatomical priori information is explicitly integrated into the self-supervised process, through auto-generated pixel-wise masks (using MRI-SegFlow: a system with unique voting processes for unsupervised deep learning-based segmentation) of vertebral bodies (VBs) and intervertebral discs (IVDs). With finetuning of a small dataset, the model can produce accurate pathology classifications. Our SpineGEM is validated on the Hong Kong Disc Degeneration Cohort (HKDDC) dataset with pathologies including Schneiderman Score, Disc Bulging, Pfirrmann Grading and Schmorl's Node. Results show that compared with training from scratch (n = 1280), the model generated through SpineGEM (n = 320) can achieve higher classification accuracy with much less supervision (similar to 5% higher on mean-precision and similar to 4% higher on mean-recall).

Automated summary

This paper introduces a hybrid-supervised model generation strategy called SpineGEM, which can efficiently produce a high-performing deep learning model for pathological classification of lumbar degenerative diseases. By utilizing a unique self-supervised learning process and integrating anatomical prior information, the model can achieve accurate pathology classifications with fine-tuning on a small dataset.