Enhanced multiple sclerosis diagnosis using high-resolution 3D OCT volumes with synthetic slices

Abstract

[Abstract]: Optical Coherence Tomography (OCT), known for its micron-level resolution and non-invasive nature, is essential in diagnosing Multiple Sclerosis (MS). Accurate MS diagnosis through OCT is facilitated by identifying changes in retinal layer thickness, which serve as biomarkers, such as the retinal nerve fiber layer and the macular ganglion cell layer. To adequately diagnose MS, it is crucial to detect thickness changes in specific small regions of the retina, necessitating extensive retinal coverage with minimal granularity. However, a significant challenge in using OCT for MS diagnosis is the inverse relationship between the number of slices and the image quality of OCT volumes. High-resolution images require longer acquisition times, which limits the number of slices that can be obtained and results in covering a smaller area of the retina. Conversely, increasing the number of slices reduces individual image quality, impairing the detection of retinal layer structures. To address this issue, we propose a novel approach for the automatic detection of MS using high-resolution synthetic 3D OCT volumes. Our methodology involves using a generative network to synthesize intermediate slices, thereby enhancing retinal coverage without degrading image quality. This synthetic augmentation improves cross-sectional resolution, providing more comprehensive retinal details. Then we use a segmentation network to extract biomarkers and multiple 3D classification models to directly detect MS automatically. Our results demonstrate significant improvements in accuracy and reliability. Integrating synthetic slices into OCT volumes enhances the detection of MS, underscoring the potential of generative models for better clinical outcomes in MS diagnosis.