| Full text | |
| Author(s): |
Benvenuto, Giovana A.
;
Colnago, Marilaine
;
Dias, Mauricio A.
;
Negri, Rogerio G.
;
Silva, Erivaldo A.
;
Casaca, Wallace
Total Authors: 6
|
| Document type: | Journal article |
| Source: | BIOENGINEERING-BASEL; v. 9, n. 8, p. 17-pg., 2022-08-01. |
| Abstract | |
In ophthalmology, the registration problem consists of finding a geometric transformation that 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 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. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 21/01305-6 - Theoretical advances on anomaly detection and environmental monitoring systems building |
| Grantee: | Rogério Galante Negri |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 19/26288-7 - Unsupervised learning for fundus image registration via convolutional neural network and optimial mass transportation |
| Grantee: | Giovana Augusta Benvenuto |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 21/03328-3 - Development of new methodologies and machine intelligence-based technological solutions for digital image segmentation and COVID-19 pandemic response |
| Grantee: | Wallace Correa de Oliveira Casaca |
| Support Opportunities: | Regular Research Grants |