Open AccessMultimodal affine registration for ICGA and MCSL fundus images of high myopia
Author(s) -
Gaohui Luo,
Xinjian Chen,
Fei Shi,
Yunzhen Peng,
Dehui Xiang,
Qiuying Chen,
Xun Xu,
Weifang Zhu,
Ying Fan
Publication year - 2020
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.393178
Subject(s) - artificial intelligence , image registration , affine transformation , computer science , computer vision , indocyanine green angiography , segmentation , convolutional neural network , fundus (uterus) , medical imaging , medicine , pattern recognition (psychology) , radiology , ophthalmology , image (mathematics) , mathematics , fluorescein angiography , visual acuity , pure mathematics
The registration between indocyanine green angiography (ICGA) and multi-color scanning laser (MCSL) imaging fundus images is vital for the joint linear lesion segmentation in ICGA and MCSL and the evaluation whether MCSL can replace ICGA as a non-invasive diagnosis for linear lesion. To our best knowledge, there are no studies focusing on the image registration between these two modalities. In this paper, we propose a framework based on convolutional neural networks for the multimodal affine registration between ICGA and MCSL images, which contains two parts: coarse registration stage and fine registration stage. In the coarse registration stage, the optic disc is segmented and its centroid is used as a matching point to perform coarse registration. The fine registration stage regresses affine parameters directly using jointly supervised and weakly-supervised loss function. Experimental results show the effectiveness of the proposed method, which lays a sound foundation for further evaluation of non-invasive diagnosis of linear lesion based on MCSL.