TY - GEN
T1 - Multi-scale U-net with Edge Guidance for Multimodal Retinal Image Deformable Registration
AU - Tian, Yuntong
AU - Hu, Yan
AU - Ma, Yuhui
AU - Hao, Huaying
AU - Mou, Lei
AU - Yang, Jianlong
AU - Zhao, Yitian
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7
Y1 - 2020/7
N2 - Registration of multimodal retinal images is of great importance in facilitating the diagnosis and treatment of many eye diseases, such as the registration between color fundus images and optical coherence tomography (OCT) images. However, it is difficult to obtain ground truth, and most existing algorithms are for rigid registration without considering the optical distortion. In this paper, we present an unsupervised learning method for deformable registration between the two images. To solve the registration problem, the structure achieves a multi-level receptive field and takes contour and local detail into account. To measure the edge difference caused by different distortions in the optics center and edge, an edge similarity (ES) loss term is proposed, so loss function is composed by local cross-correlation, edge similarity and diffusion regularizer on the spatial gradients of the deformation matrix. Thus, we propose a multi-scale input layer, U-net with dilated convolution structure, squeeze excitation (SE) block and spatial transformer layers. Quantitative experiments prove the proposed framework is best compared with several conventional and deep learningbased methods, and our ES loss and structure combined with Unet and multi-scale layers achieve competitive results for normal and abnormal images.
AB - Registration of multimodal retinal images is of great importance in facilitating the diagnosis and treatment of many eye diseases, such as the registration between color fundus images and optical coherence tomography (OCT) images. However, it is difficult to obtain ground truth, and most existing algorithms are for rigid registration without considering the optical distortion. In this paper, we present an unsupervised learning method for deformable registration between the two images. To solve the registration problem, the structure achieves a multi-level receptive field and takes contour and local detail into account. To measure the edge difference caused by different distortions in the optics center and edge, an edge similarity (ES) loss term is proposed, so loss function is composed by local cross-correlation, edge similarity and diffusion regularizer on the spatial gradients of the deformation matrix. Thus, we propose a multi-scale input layer, U-net with dilated convolution structure, squeeze excitation (SE) block and spatial transformer layers. Quantitative experiments prove the proposed framework is best compared with several conventional and deep learningbased methods, and our ES loss and structure combined with Unet and multi-scale layers achieve competitive results for normal and abnormal images.
KW - Color Fundus
KW - Deep Learning
KW - Deformable Registration
KW - Multimodal Registration
KW - Optical Coherence Tomography
UR - http://www.scopus.com/inward/record.url?scp=85091036155&partnerID=8YFLogxK
U2 - 10.1109/EMBC44109.2020.9175613
DO - 10.1109/EMBC44109.2020.9175613
M3 - Conference contribution
AN - SCOPUS:85091036155
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1360
EP - 1363
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -