Performance Evaluation of Retinal OCT Fluid Segmentation, Detection and Generalisation over Variations of Data Sources

Abstract

Retinal Optical Coherence Tomography (OCT) is a non-invasive cross-sectional scan of the eye that provides qualitative 3D visualization of the retinal anatomy. It is used to study the retinal structure and the presence of pathogens. The advent of retinal OCT has transformed ophthalmology and is currently paramount for the diagnosis, monitoring, and treatment of many eye diseases, including macular edema, which impairs vision severely, and glaucoma, which can cause irreversible blindness. However, the quality of OCT images can vary among device manufacturers. Deep learning methods have been successful in the medical image segmentation community, but it is not yet clear if the level of success can be generalized across images collected from different device vendors. In this study, we provide a comprehensive review of current deep learning segmentation methods applied to OCT images. Furthermore, to investigate the problem of variant of data sources from OCT device vendors, we analyse a selection of the most representative methods to address this problem, including those on the top of the RETOUCH competition such as nnUNet and its variant nnUNet_RASPP, SAM and its variant SAMedOCT, IAUNet_SPP_CL, alongside other state-of-the-art algorithms. The algorithms were validated on the RETOUCH challenge dataset, which was acquired from three device vendors across three medical centers from patients suffering from two retinal disease types. Experimental results show that for several tasks of segmentation, detection and generalisation performance from the retinal images, while fine-tuned large foundation models such as SAMedOCT have demonstrated promising performance, the specifically designed and trained models such as nnUNet and nnUNet_RASPP still offer a slight advantage overall. Also, the nnUNet_RASPP obtained the best performance of 82.3% of mean Dice score for fluid segmentation.