Multi-source Unsupervised Domain Adaptation Fundus Lesion Segmentation of Various OCT Devices with Moment Consistency

Accurate segmentation of lesion in fundus OCT images an assist ophthalmologists to determine the degree of retinopathy and choroidopathy. However, OCT images are often acquired from various manufacturers' OCT devices, which is challenging for traditional models due to domain shift. In this paper, a novel multi-source domain adaptation framework is designed to address the challenge of segmenting fundus lesions in OCT images acquired from devices produced by different manufacturers with three core methodological innovations: (1) A multi-order moment consistency approach using moment generating function (MGF) to align feature distributions across domains. By approximating multi-order central moments using derivatives of the MGF, our method theoretically enables efficient alignment of high-order statistical features without explicit computation of polynomial expansions. (2) A perturbation-based feature consistency strategy to improve model robustness. By using segmentation and moment losses to guide perturbation generation, our method explicitly links semantic consistency with feature distribution alignment. (3) A population stability whitening technique to separate style-related and content-related features. By analyzing covariance matrix variances across perturbations, our method attempts to automatically separate style and content features. Our method is compared with several state-of-the-art approaches on two datasets, comprising diverse domains collected from various manufacturers' OCT devices. Experimental results clearly demonstrate the significant superiority of our method.