UNSUPERVISED DEEP LEARNING NETWORK FOR DEFORMABLE FUNDUS IMAGE REGISTRATION

Author(s)

Benvenuto, Giovana Augusta ; Colnago, Marilaine ; Casaca, Wallace ; IEEE

• Document type: Journal article
• Source: 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP); v. N/A, p. 5-pg., 2022-01-01.

Abstract

In ophthalmology and vision science applications, the process of registering a pair of fundus images, captured at different scales and viewing angles, is of paramount importance to support the diagnosis of diseases and routine eye examinations. Aiming at addressing the retina registration problem from the Deep Learning perspective, in this paper we introduce an end-to-end framework capable of learning the registration task in a fully unsupervised way. The designed approach combines Convolutional Neural Networks and Spatial Transformation Network into a unified pipeline that takes a similarity metric to gauge the difference between the images, thus enabling the image alignment without requiring any ground-truth data. Once the model is fully trained, it can perform one-shot registrations by just providing as input the pair of fundus images. As shown in the validation study, the trained model is able to successfully deal with several categories of fundus images, surpassing other recent techniques for retina registration. (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: 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