OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing

• Published in: Information processing in medical imaging : proceedings of the ... conference Vol. 13939; p. 415
• Main Authors: Zhu, Wenhui, Qiu, Peijie, Dumitrascu, Oana M, Sobczak, Jacob M, Farazi, Mohammad, Yang, Zhangsihao, Nandakumar, Keshav, Wang, Yalin
• Format: Journal Article
• Language: English
• Published: Germany 01.01.2023
• Subjects: Retinal color fundus photography; Regularization by enhancing; Optimal transport; Image enhancement; Unsupervised learning
• ISSN: 1011-2499
• DOI: 10.1007/978-3-031-34048-2_32

Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes. Optimal retinal image quality is mandated for accurate medical diagnoses and automated analyses. Herein, we leveraged the theory to propose an unpaired image-to-image translation scheme for mapping low-quality retinal CFPs to high-quality counterparts. Furthermore, to improve the flexibility, robustness, and applicability of our image enhancement pipeline in the clinical practice, we generalized a state-of-the-art model-based image reconstruction method, regularization by denoising, by plugging in priors learned by our OT-guided image-to-image translation network. We named it as . We validated the integrated framework, OTRE, on three publicly available retinal image datasets by assessing the quality after enhancement and their performance on various downstream tasks, including diabetic retinopathy grading, vessel segmentation, and diabetic lesion segmentation. The experimental results demonstrated the superiority of our proposed framework over some state-of-the-art unsupervised competitors and a state-of-the-art supervised method.