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We introduce a system that allows the immediate identification and inspection of fat and muscle structures around the lumbar spine as a means of orthopaedic diagnostics before surgical treatment. The system comprises a backend component that accepts MRI data from a web-based interactive frontend as REST requests. The MRI data is passed through a U-net model, fine-tuned on lumbar MRI images, to generate segmentation masks of fat and muscle areas. The result is sent back to the frontend that functions as an inspection tool. For the model training, 4000 MRI images from 108 patients were used in a k-fold cross-validation study with k = 10. The model training was performed over 25-30 epochs. We applied shift, scale, and rotation operations as well as elastic deformation and distortion functions for image augmentation and a combined objective function using Dice and Focal loss. The trained models reached a mean dice score of 0.83 and 0.52 and a mean area error tissue of 0.1 and 0.3 for muscle and fat tissue, respectively. The interactive webbased frontend as an inspection tool was evaluated by clinicians to be suitable for the exploration of patient data as well as the assessment of segmentation results. We developed a system that uses semantic segmentation to identify fat and muscle tissue areas in MRI images of the lumbar spine. Further improvements should focus on the segmentation accuracy of fat tissue, as it is a determining factor in surgical decisionmaking. To our knowledge, this is the first system that automatically provides semantic information of the respective lumbar tissues.

An interactive system for muscle and fat tissue identification of the lumbar spine using semantic segmentation