Recognition of the lumbar spine using MRI plane-based FiLM conditioning and patient dependent batching on semantic segmentation

Abstract

Lower back pain (LBP) is a common symptom that can be linked to the degeneration of intervertebral discs or injuries to vertebrae. Accurate segmentation of lumbar spinal structures is vital for image-based surgery planning but can be resource intensive. Recent studies have shown that U-Net models can be used reliably to segment vertebrae and intervertebral discs from medical imaging data. This study investigated the potential of combining multiple planar MRI images to segment these structures automatically. The study used MRI data from 83 patients with back pain of the lower spine, with ground truth segmentations performed by a spine surgery specialist using Mimics software. The segmentation models were trained using a specialized patient batching method and a FiLMed U-Net model, which applies a Feature-wise Linear Modulation layer to improve mask generation. The models were trained using a 10-fold cross validation study, and the dice loss was used as the objective function. The study found that both patient batching and FiLM layers improve results by around 5% compared to a baseline U-Net. The average IoU scores of the patient batching method were 0.896 for vertebrae and 0.876 intervertebral disc segmentation. The FiLMed U-Net model achieved average IoU scores of 0.844 and 0.814 and against a baseline of 0.837 and 0.774, respectively. A web-based tool was developed to enable medical personnel to assess segmentation quality visually. Overall, the study indicates that U-Nets show better segmentation potential when using imaging data with complete 3D information of one patient. Future studies should focus on increasing the dataset size and exploring other 3D model architectures to improve segmentation performance.

Keywords

• semantic segmentation
• machine learning
• unet
• tissue diagnostics
• film