TY - GEN
T1 - OTRE
T2 - 28th International Conference on Information Processing in Medical Imaging, IPMI 2023
AU - Zhu, Wenhui
AU - Qiu, Peijie
AU - Dumitrascu, Oana M.
AU - Sobczak, Jacob M.
AU - Farazi, Mohammad
AU - Yang, Zhangsihao
AU - Nandakumar, Keshav
AU - Wang, Yalin
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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 Optimal Transport (OT) 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 regularization by enhancing (RE). 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.
AB - 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 Optimal Transport (OT) 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 regularization by enhancing (RE). 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.
KW - Image enhancement
KW - Optimal transport
KW - Regularization by enhancing
KW - Retinal color fundus photography
KW - Unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85163951422&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85163951422&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-34048-2_32
DO - 10.1007/978-3-031-34048-2_32
M3 - Conference contribution
AN - SCOPUS:85163951422
SN - 9783031340475
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 415
EP - 427
BT - Information Processing in Medical Imaging - 28th International Conference, IPMI 2023, Proceedings
A2 - Frangi, Alejandro
A2 - de Bruijne, Marleen
A2 - Wassermann, Demian
A2 - Navab, Nassir
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 June 2023 through 23 June 2023
ER -