Luo Gaohui, Chen Xinjian, Shi Fei, Peng Yunzhen, Xiang Dehui, Chen Qiuying, Xu Xun, Zhu Weifang, Fan Ying
School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China.
contributed equally.
Biomed Opt Express. 2020 Jul 20;11(8):4443-4457. doi: 10.1364/BOE.393178. eCollection 2020 Aug 1.
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.
吲哚菁绿血管造影（ICGA）与多色扫描激光（MCSL）眼底图像之间的配准对于ICGA和MCSL中联合线性病变分割以及评估MCSL是否可作为线性病变的非侵入性诊断替代ICGA至关重要。据我们所知，尚无研究关注这两种模态之间的图像配准。在本文中，我们提出了一种基于卷积神经网络的框架，用于ICGA和MCSL图像之间的多模态仿射配准，该框架包含两个部分：粗配准阶段和精配准阶段。在粗配准阶段，分割视盘并将其质心用作匹配点进行粗配准。精配准阶段使用联合监督和弱监督损失函数直接回归仿射参数。实验结果表明了所提方法的有效性，为基于MCSL的线性病变非侵入性诊断的进一步评估奠定了良好基础。
