Multiscale Unsupervised Retinal Edema Area Segmentation in OCT Images

Abstract

Retinal edema area, which can be observed in the non-invasive optical coherence tomography image, is essential for the diagnosis and treatment of many retinal diseases. Due to the demand of professional knowledge for its annotation, acquiring sufficient labeled data for the usual data-driven learning-based approaches is time-consuming and laborious. To alleviate the intensive workload for manual labeling, unsupervised learning technique has been widely explored and adopted in different applications. However, the corresponding research in medical image segmentation is still limited and the performance is unsatisfactory. In this paper, we propose a novel unsupervised segmentation framework, which consists of two stages: the image-level clustering to group images into different categories and the pixel-level segmentation which leverages the guidance of the clustering network. Based on the observation that smaller lesions are more obvious on large scale images with detail texture information and larger lesions are easier to capture on small scale images for the large field-of-view, we introduce multiscale information into both stages through a scale-invariant regularization and a multiscale Class Activation Map (CAM) fusing strategy, respectively. Experiments on the public retinal dataset show that the proposed framework achieves a 76.28% Dice score without any supervision, which outperforms state-of-the-art unsupervised approaches by a large margin (more than 20% improvement in Dice score).