Medical Image Registration Using Deep Neural Networks: A Comprehensive
Review
- URL: http://arxiv.org/abs/2002.03401v1
- Date: Sun, 9 Feb 2020 17:22:05 GMT
- Title: Medical Image Registration Using Deep Neural Networks: A Comprehensive
Review
- Authors: Hamid Reza Boveiri, Raouf Khayami, Reza Javidan, Ali Reza MehdiZadeh
- Abstract summary: State-of-the-art literature on medical image registration using deep neural networks is presented.
The review is systematic and encompasses all the related works previously published in the field.
This review allows a deep understanding and insight for the readers active in the field who are investigating the state-of-the-art and seeking to contribute the future literature.
- Score: 1.2425910171551517
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image-guided interventions are saving the lives of a large number of patients
where the image registration problem should indeed be considered as the most
complex and complicated issue to be tackled. On the other hand, the recently
huge progress in the field of machine learning made by the possibility of
implementing deep neural networks on the contemporary many-core GPUs opened up
a promising window to challenge with many medical applications, where the
registration is not an exception. In this paper, a comprehensive review on the
state-of-the-art literature known as medical image registration using deep
neural networks is presented. The review is systematic and encompasses all the
related works previously published in the field. Key concepts, statistical
analysis from different points of view, confiding challenges, novelties and
main contributions, key-enabling techniques, future directions and prospective
trends all are discussed and surveyed in details in this comprehensive review.
This review allows a deep understanding and insight for the readers active in
the field who are investigating the state-of-the-art and seeking to contribute
the future literature.
Related papers
- Medical Image Registration and Its Application in Retinal Images: A Review [4.634056717325716]
We provide a comprehensive review of medical image registration methods from traditional and deep learning-based directions.
We also discuss the current challenges of retinal image registration and provide insights and prospects for future research.
arXiv Detail & Related papers (2024-03-25T07:35:28Z) - Medical image registration using unsupervised deep neural network: A
scoping literature review [0.9527960631238173]
In medicine, image registration is vital in image-guided interventions and other clinical applications.
The implementation of deep neural networks provides an opportunity for some medical applications such as conducting image registration in less time with high accuracy.
arXiv Detail & Related papers (2022-08-03T03:11:34Z) - Deep Image Deblurring: A Survey [165.32391279761006]
Deblurring is a classic problem in low-level computer vision, which aims to recover a sharp image from a blurred input image.
Recent advances in deep learning have led to significant progress in solving this problem.
arXiv Detail & Related papers (2022-01-26T01:31:30Z) - Neural Fields in Visual Computing and Beyond [54.950885364735804]
Recent advances in machine learning have created increasing interest in solving visual computing problems using coordinate-based neural networks.
neural fields have seen successful application in the synthesis of 3D shapes and image, animation of human bodies, 3D reconstruction, and pose estimation.
This report provides context, mathematical grounding, and an extensive review of literature on neural fields.
arXiv Detail & Related papers (2021-11-22T18:57:51Z) - A Deep Discontinuity-Preserving Image Registration Network [73.03885837923599]
Most deep learning-based registration methods assume that the desired deformation fields are globally smooth and continuous.
We propose a weakly-supervised Deep Discontinuity-preserving Image Registration network (DDIR) to obtain better registration performance and realistic deformation fields.
We demonstrate that our method achieves significant improvements in registration accuracy and predicts more realistic deformations, in registration experiments on cardiac magnetic resonance (MR) images.
arXiv Detail & Related papers (2021-07-09T13:35:59Z) - Recent advances and clinical applications of deep learning in medical
image analysis [7.132678647070632]
We reviewed and summarized more than 200 recently published papers to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks.
Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical images.
arXiv Detail & Related papers (2021-05-27T18:05:12Z) - Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing.
In this paper, we develop a novel generative method named generative adversarial U-Net.
Our newly designed model is domain-free and generalizable to various medical images.
arXiv Detail & Related papers (2021-01-12T23:02:26Z) - Few-shot Medical Image Segmentation using a Global Correlation Network
with Discriminative Embedding [60.89561661441736]
We propose a novel method for few-shot medical image segmentation.
We construct our few-shot image segmentor using a deep convolutional network trained episodically.
We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
arXiv Detail & Related papers (2020-12-10T04:01:07Z) - Trends in deep learning for medical hyperspectral image analysis [2.2404871878551353]
This review paper examines publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery.
This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging.
arXiv Detail & Related papers (2020-11-27T19:42:06Z) - Domain Generalization for Medical Imaging Classification with
Linear-Dependency Regularization [59.5104563755095]
We introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification.
Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding.
arXiv Detail & Related papers (2020-09-27T12:30:30Z) - A review of deep learning in medical imaging: Imaging traits, technology
trends, case studies with progress highlights, and future promises [27.16172003905426]
We first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging.
We then present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging.
arXiv Detail & Related papers (2020-08-02T14:26:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.