Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net (MU-Net) on Spine Magnetic Resonance Images.

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere. Due to its ability to produce a detailed view of the soft tissues, including the spinal cord, nerves, intervertebral discs, and vertebrae, Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine. The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases. It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues, including muscles, ligaments, and intervertebral discs. U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation accuracy. This work proposes a modified U-Net architecture namely MU-Net, consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra S1. Pseudo-colour mask images were generated and used as ground truth for training the model. The work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley Data. The proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79% of pixel accuracy (PA), 98.66% of dice similarity coefficient (DSC), 97.36% of Jaccard coefficient, and 92.55% mean Intersection over Union (mean IoU) metrics using the mentioned dataset. [ABSTRACT FROM AUTHOR]