Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach
This work addresses the problem of interpreting deep learning-based fundus image registration for clinicians, though it appears incremental by building on existing methods to enhance analysis.
The paper tackled the challenging task of registering fundus images for disease monitoring by proposing an imitation learning framework with a divide-and-conquer approach to improve interpretability, achieving a reduction in target registration error of up to 95%.
Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions. The differences between intra-patient images can be assessed quantitatively by registering serial acquisitions. Due to the variability of the images (i.e. contrast, luminosity) and the anatomical changes of the retina, the registration of fundus images remains a challenging task. Recently, several deep learning approaches have been proposed to register fundus images in an end-to-end fashion, achieving remarkable results. However, the results are difficult to interpret and analyze. In this work, we propose an imitation learning framework for the registration of 2D color funduscopic images for a wide range of applications such as disease monitoring, image stitching and super-resolution. We follow a divide-and-conquer approach to improve the interpretability of the proposed network, and analyze both the influence of the input image and the hyperparameters on the registration result. The results show that the proposed registration network reduces the initial target registration error up to 95\%.