Shen Jiayan, Chen Zhongyue, Peng Yuanyuan, Zhang Siqi, Xu Chenan, Zhu Weifang, Liu Haiyun, Chen Xinjian
MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China.
Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai, China.
Med Phys. 2023 Aug;50(8):4839-4853. doi: 10.1002/mp.16294. Epub 2023 Mar 4.
Choroid neovascularization (CNV) has no obvious symptoms in the early stage, but with its gradual expansion, leakage, rupture, and bleeding, it can cause vision loss and central scotoma. In some severe cases, it will lead to permanent visual impairment.
Accurate prediction of disease progression can greatly help ophthalmologists to formulate appropriate treatment plans and prevent further deterioration of the disease. Therefore, we aim to predict the growth trend of CNV to help the attending physician judge the effectiveness of treatment.
In this paper, we develop a CNN-based method for CNV growth prediction. To achieve this, we first design a registration network to rigidly register the spectral domain optical coherence tomography (SD-OCT) B-scans of each subject at different time points to eliminate retinal displacements of longitudinal data. Then, considering the correlation of longitudinal data, we propose a co-segmentation network with a correlation attention guidance (CAG) module to cooperatively segment CNV lesions of a group of follow-up images and use them as input for growth prediction. Finally, based on the above registration and segmentation networks, an encoder-recurrent-decoder framework is developed for CNV growth prediction, in which an attention-based gated recurrent unit (AGRU) is embedded as the recurrent neural network to recurrently learn robust representations.
The registration network rigidly registers the follow-up images of patients to the reference images with a root mean square error (RMSE) of 6.754 pixels. And compared with other state-of-the-art segmentation methods, the proposed segmentation network achieves high performance with the Dice similarity coefficients (Dsc) of 85.27%. Based on the above experiments, the proposed growth prediction network can play a role in predicting the future CNV morphology, and the predicted CNV has a Dsc of 83.69% with the ground truth, which is significantly consistent with the actual follow-up visit.
The proposed registration and segmentation networks provide the possibility for growth prediction. In addition, accurately predicting the growth of CNV enables us to know the efficacy of the drug against individuals in advance, creating opportunities for formulating appropriate treatment plans.
脉络膜新生血管（CNV）在早期没有明显症状，但随着其逐渐扩张、渗漏、破裂和出血，可导致视力丧失和中心暗点。在一些严重情况下，会导致永久性视力损害。
准确预测疾病进展可极大地帮助眼科医生制定合适的治疗方案并防止疾病进一步恶化。因此，我们旨在预测CNV的生长趋势，以帮助主治医生判断治疗效果。
在本文中，我们开发了一种基于卷积神经网络（CNN）的CNV生长预测方法。为此，我们首先设计一个配准网络，对每个受试者在不同时间点的光谱域光学相干断层扫描（SD-OCT）B扫描进行刚性配准，以消除纵向数据的视网膜位移。然后，考虑到纵向数据的相关性，我们提出了一种带有相关注意力引导（CAG）模块的协同分割网络，用于对一组随访图像中的CNV病变进行协同分割，并将其用作生长预测的输入。最后，基于上述配准和分割网络，开发了一个用于CNV生长预测的编码器-循环-解码器框架，其中嵌入了一个基于注意力的门控循环单元（AGRU）作为循环神经网络，以循环学习鲁棒表示。
配准网络将患者的随访图像与参考图像进行刚性配准，均方根误差（RMSE）为6.754像素。与其他现有分割方法相比，所提出的分割网络具有85.27%的骰子相似系数（Dsc），实现了高性能。基于上述实验，所提出的生长预测网络能够在预测未来CNV形态方面发挥作用，预测得到的CNV与真实情况的Dsc为83.69%，与实际随访情况显著一致。
所提出的配准和分割网络为生长预测提供了可能性。此外，准确预测CNV的生长使我们能够提前了解药物对个体的疗效，为制定合适的治疗方案创造机会。
