Synthetic Generation of Intermediate MR Images of Lumbar Spine Stenosis for Segmentation

Lumbar spinal stenosis is a narrowing or constriction in the lumbar spine. Magnetic Resonance Imaging (MRI) is preferred for its diagnosis. Limited research has been conducted with the intent of making synthetic and pseudo in-painting datasets. The objective of this study was to develop a model to create a slice of MR image in the subsequent spinal disc, given an MR image slice from previous disc. To the best of our knowledge, this study is the first to explore the effectiveness of creating pseudo in-painting in the datasets for Lumbar Spine Stenosis. We selected a publicly available dataset, named Lumbar Spine MR images Dataset, for reproducibility. We used 1,545 composite T1 and T2 MR images (combined) in the axial view. The combined images were of 515 patients, each having the lower three levels (L3 to L5) of the lumbar spine. Broadly, our method is Generative Adversarial Network that we modified for our task. Our accuracy metric was the DICE Score. The accuracy was between 88.66 and 90.85%. We claim that our work is significant, as it establishes the ability of GAN to perform in-painting based on a slice for a disc, which indicates possibilities of future research on in-painting of larger stack of MR images and expansive synthetic dataset generation.