A Practical Review on Medical Image Registration: From Rigid to Deep Learning Based Approaches

Andrade, Natan; Faria, Fábio A.; Cappabianco, Fábio A. M.

doi:10.1109/sibgrapi.2018.00066

Cited by 27 publications

(18 citation statements)

References 51 publications

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“…All the objects used are stored in a container called toolbox. However, contents can be added to the container using the register method [126].…”

Section: Proposed Optimisation Model Based On Multi Objective Genementioning

confidence: 99%

A Novel and Reliable Framework of Patient Deterioration Prediction in Intensive Care Unit Based on Long Short-Term Memory-Recurrent Neural Network

et al. 2021

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“…All the objects used are stored in a container called toolbox. However, contents can be added to the container using the register method [126].…”

Section: Proposed Optimisation Model Based On Multi Objective Genementioning

confidence: 99%

A Novel and Reliable Framework of Patient Deterioration Prediction in Intensive Care Unit Based on Long Short-Term Memory-Recurrent Neural Network

et al. 2021

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“…The aim of this review is to provide a critical overview of existing literature on DL-based image registration, by highlighting innovations from a methodological and functional perspective, discussing current trends, challenges and limitations, and providing insights to the possible directions for future research. While there have been several review papers published recently on DL-based medical image registration [16][17][18][19], they primarily focus on the architecture of networks proposed for DL-based medical image registration, grouping and discussing them according to their design and learning paradigms (i.e. supervised, weakly-supervised or unsupervised, for example).…”

Section: Basic Deep Learning Networkmentioning

confidence: 99%

Deep learning in medical image registration

et al. 2020

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Image registration is a fundamental task in multiple medical image analysis applications. With the advent of deep learning, there have been significant advances in algorithmic performance for various computer vision tasks in recent years, including medical image registration. The last couple of years have seen a dramatic increase in the development of deep learning-based medical image registration algorithms. Consequently, a comprehensive review of the current state-of-the-art algorithms in the field is timely, and necessary. This review is aimed at understanding the clinical applications and challenges that drove this innovation, analysing the functionality and limitations of existing approaches, and at providing insights to open challenges and as yet unmet clinical needs that could shape future research directions. To this end, the main contributions of this paper are: (a) discussion of all deep learning-based medical image registration papers published since 2013 with significant methodological and/or functional contributions to the field; (b) analysis of the development and evolution of deep learning-based image registration methods, summarising the current trends and challenges in the domain; and (c) overview of unmet clinical needs and potential directions for future research in deep learning-based medical image registration.

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“…Nonlinear registration (Ashburner, 2007;Avants et al, 2008;Rueckert et al, 1999;Vercauteren et al, 2009) estimates local deformations between pairs of images, and these algorithms tend to produce more accurate estimates when they can focus entirely on the anatomy of interest (Klein et al, 2009;Ou et al, 2014). Similarly, skull-stripping increases the reliability of linear registration (Cox and Jesmanowicz, 1999;Friston et al, 1995;Hoffmann et al, 2015;Jenkinson and Smith, 2001;Jiang et al, 1995;Modat et al, 2014;Reuter et al, 2010) by excluding anatomy that deforms non-rigidly, such as the eyes, jaw, and tongue (Andrade et al, 2018;Fein et al, 2006;Fischmeister et al, 2013;Hoffmann et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

SynthStrip: Skull-Stripping for Any Brain Image

Hoopes,

Mora,

Dalca

et al. 2022

Preprint

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The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skullstripping methods are usually tailored to images with specific acquisition properties, namely near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are prevalent in research settings. As a result, existing tools tend to adapt poorly to other image types, such as stacks of thick slices acquired with fast spin-echo (FSE) MRI that are common in the clinic. While learning-based approaches for brain extraction have gained traction in recent years, these methods face a similar burden, as they are only effective for image types seen during the training procedure. To achieve robust skull-stripping across a landscape of protocols, we introduce SynthStrip, a rapid, learning-based brain-extraction tool. By leveraging anatomical segmentations to generate an entirely synthetic training dataset with anatomies, intensity distributions, and artifacts that far exceed the realistic range of medical images, SynthStrip learns to successfully generalize to a variety of real acquired brain images, removing the need for training data with target contrasts. We demonstrate the efficacy of SynthStrip for a diverse set of image acquisitions and resolutions across subject populations, ranging from newborn to adult. We show substantial improvements in accuracy over popular skull-stripping baselines -all with a single trained model. Our method and labeled evaluation data are available at https://w3id.org/synthstrip.

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A Practical Review on Medical Image Registration: From Rigid to Deep Learning Based Approaches

Cited by 27 publications

References 51 publications

A Novel and Reliable Framework of Patient Deterioration Prediction in Intensive Care Unit Based on Long Short-Term Memory-Recurrent Neural Network

A Novel and Reliable Framework of Patient Deterioration Prediction in Intensive Care Unit Based on Long Short-Term Memory-Recurrent Neural Network

Deep learning in medical image registration

SynthStrip: Skull-Stripping for Any Brain Image

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