When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)

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Title: When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)
Authors: Villena Martínez, Víctor | Oprea, Sergiu | Saval-Calvo, Marcelo | Azorin-Lopez, Jorge | Fuster-Guilló, Andrés | Fisher, Robert B.
Research Group/s: Informática Industrial y Redes de Computadores | Arquitecturas Inteligentes Aplicadas (AIA)
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Registration | 3D alignment | Neural networks | Deep Registration Networks
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: 26-Oct-2020
Publisher: MDPI
Citation: Villena-Martinez V, Oprea S, Saval-Calvo M, Azorin-Lopez J, Fuster-Guillo A, Fisher RB. When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs). Applied Sciences. 2020; 10(21):7524. https://doi.org/10.3390/app10217524
Abstract: This paper reviews recent deep learning-based registration methods. Registration is the process that computes the transformation that aligns datasets, and the accuracy of the result depends on multiple factors. The most significant factors are the size of input data; the presence of noise, outliers and occlusions; the quality of the extracted features; real-time requirements; and the type of transformation, especially those defined by multiple parameters, such as non-rigid deformations. Deep Registration Networks (DRNs) are those architectures trying to solve the alignment task using a learning algorithm. In this review, we classify these methods according to a proposed framework based on the traditional registration pipeline. This pipeline consists of four steps: target selection, feature extraction, feature matching, and transform computation for the alignment. This new paradigm introduces a higher-level understanding of registration, which makes explicit the challenging problems of traditional approaches. The main contribution of this work is to provide a comprehensive starting point to address registration problems from a learning-based perspective and to understand the new range of possibilities.
Sponsor: This work was supported by the Spanish State Research Agency (AEI) and the European Regional Development Fund (FEDER) under project TIN2017-89069-R. This work was also supported by a Valencian Regional project (GV/2020/056), two Valencian Grants for Ph.D. studies (ACIF/2017/223 and ACIF/2018/197), and two Valencian Grants for predoctoral internships (BEFPI/2020/001 and BEFPI/2020/068).
URI: http://hdl.handle.net/10045/110387
ISSN: 2076-3417
DOI: 10.3390/app10217524
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: https://doi.org/10.3390/app10217524
Appears in Collections:INV - I2RC - Artículos de Revistas
INV - AIA - Artículos de Revistas

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