A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration

A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration

Blog Article

In ophthalmology, the registration problem consists of finding a geometric transformation that raceline renegade 8 16x10 aligns a pair of images, supporting eye-care specialists who need to record and compare images of the same patient.Considering the registration methods for handling eye fundus images, the literature offers only a limited number of proposals based on deep learning (DL), whose implementations use the supervised learning paradigm to train a model.Additionally, ensuring high-quality registrations while still being flexible enough to tackle a broad range of fundus images is another drawback faced by most existing methods in the literature.Therefore, in this paper, we address the above-mentioned issues by introducing a new DL-based framework for eye fundus registration.Our methodology combines a U-shaped fully convolutional neural network with a spatial transformation learning scheme, where a reference-free similarity metric allows the galaxy buds skin registration without assuming any pre-annotated or artificially created data.

Once trained, the model is able to accurately align pairs of images captured under several conditions, which include the presence of anatomical differences and low-quality photographs.Compared to other registration methods, our approach achieves better registration outcomes by just passing as input the desired pair of fundus images.