Fundus Vascular Segmentation Based on Data Enhancement and Invariant Feature Extraction

Abstract

Deep neural networks have emerged as the predominant method for medical image segmentation, owing to their robust feature learning capabilities, enabling accurate automatic segmentation of target structures within intricate medical images. Fundus images, being crucial in diagnosing ophthalmic diseases, underscore the importance of effective segmentation techniques. However, fundus vascular images pose challenges due to their high complexity and subtle individual differences, necessitating improvement in existing segmentation methodologies for enhanced disease classification accuracy. This paper introduces a fundus blood vessel segmentation model, employing data augmentation and invariant feature extraction, to systematically tackle the core challenges in medical image processing, particularly in fundus blood vessel segmentation. These challenges include limited source domain samples and inadequate model domain generalization. The model adopts a dual-dimensional strategy. Firstly, it delves into data augmentation technology to enhance the diversity and representativeness of samples within the finite source domain. This is achieved through an image enhancement module based on Fourier transform, mitigating the impact of data scarcity on model training effectiveness. Secondly, the research focuses on interdomain invariant feature extraction, aiming to extract feature representations that consistently characterize fundus blood vessel structure and pathology across different data distributions, thereby enhancing model generalization performance in unfamiliar domains. Specifically, the paper designs a fundus image enhancement module based on Fourier transform in the data augmentation dimension. In the feature extraction dimension, it proposes a normalization module based on uncertainty theory, departing from traditional normalization methods. Experimental results demonstrate the efficacy of the proposed method, showcasing superior generalization performance compared to existing techniques in retinal blood vessel and OD/OC segmentation tasks. Experience validates the model-agnostic nature of the learned strategy, indicating its potential for seamless transferability to other models, thereby offering robust support for advancing medical image segmentation research and applications.