Segmentation guided dual-branch classification for measuring fat infiltration in paraspinal muscles

Muscle fat infiltration (FI) is a significant change in muscle degeneration. In particular, fat infiltration in paraspinal muscles (PSMs) indicates lumbar degenerative diseases. Thus, classifying different grades of FI in PSMs plays an important role in diagnosing the relevant lumbar diseases. Recently, many deep-learning-based methods have been introduced into medical image tasks. However, such methods for classifying the grades of FI in PSMs have not been explored. In this case, this paper aims to involve deep-learning methods in grade classification for FI in PSMs. Firstly, we construct a PSMsFIGC dataset for deep learning exploration. Our PSMsFIGC dataset contains 4 grades for classification and the corresponding PSMs segmentation masks as assistance. Additionally, we propose a segmentation guided dual-branch classification framework (SGDC) to assist radiologists in confirming the grade of FI in PSMs. The structure mainly consists of a segmentation branch and a classification branch. The segmentation branch is designed to suppress the influence of irrelevant muscles for final classification. We further design a critical area indicator based on the prediction of the segmentation branch to involve more related crucial areas for the classification branch and thus bridge the two branches. However, we find that inter-class disturbance, caused by PSMs’ similar shape and features, makes the network easily fall into local optimal. Therefore, we propose a disturbance weakening module to relieve the disturbance. Extensive experiments show that our SGDC can surpass existing classific classification networks, e.g., the proposed method achieves an impressive accuracy of 89.0% on the PSMsFIGC dataset. Our dataset and code will be released at https://github.com/myjianghao/Segmentation-Guided-Dual-branch-Classification-Framework-SGDC-.