Medical image registration using deep neural networks: A comprehensive review

Boveiri, Hamid Reza; Khayami, Raouf; Javidan, Reza; Mehdizadeh, Ali

doi:10.1016/j.compeleceng.2020.106767

Cited by 135 publications

(77 citation statements)

References 36 publications

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“…where M l , F l are the warped moving landmarks and fixed landmarks respectively. The TRE, together with the Dice coefficient, are the most frequently applied evaluation metrics in the medical image registration [33]. However, the situation in the presented study is quite different.…”

Section: Target Registration Errormentioning

confidence: 95%

Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization

Wodziński

Ciepiela

Kuszewski

et al. 2021

Sensors

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Breast-conserving surgery requires supportive radiotherapy to prevent cancer recurrence. However, the task of localizing the tumor bed to be irradiated is not trivial. The automatic image registration could significantly aid the tumor bed localization and lower the radiation dose delivered to the surrounding healthy tissues. This study proposes a novel image registration method dedicated to breast tumor bed localization addressing the problem of missing data due to tumor resection that may be applied to real-time radiotherapy planning. We propose a deep learning-based nonrigid image registration method based on a modified U-Net architecture. The algorithm works simultaneously on several image resolutions to handle large deformations. Moreover, we propose a dedicated volume penalty that introduces the medical knowledge about tumor resection into the registration process. The proposed method may be useful for improving real-time radiation therapy planning after the tumor resection and, thus, lower the surrounding healthy tissues’ irradiation. The data used in this study consist of 30 computed tomography scans acquired in patients with diagnosed breast cancer, before and after tumor surgery. The method is evaluated using the target registration error between manually annotated landmarks, the ratio of tumor volume, and the subjective visual assessment. We compare the proposed method to several other approaches and show that both the multilevel approach and the volume regularization improve the registration results. The mean target registration error is below 6.5 mm, and the relative volume ratio is close to zero. The registration time below 1 s enables the real-time processing. These results show improvements compared to the classical, iterative methods or other learning-based approaches that do not introduce the knowledge about tumor resection into the registration process. In future research, we plan to propose a method dedicated to automatic localization of missing regions that may be used to automatically segment tumors in the source image and scars in the target image.

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Section: Target Registration Errormentioning

confidence: 95%

Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization

Wodziński

Ciepiela

Kuszewski

et al. 2021

Sensors

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“…In addition, a recent study investigated the development and validated a robust and accurate registration pipeline for automatic contouring for online adaptive Intensity-Modulated Proton therapy (IMPT) for prostate cancer applications [74]. There are a plethora of registration applications in medical imaging utilizing DNNs [75].…”

Section: Registrationmentioning

confidence: 99%

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

et al. 2021

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Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI) field. Modern radiation oncology is based on the exploitation of advanced computational methods aiming to personalization and high diagnostic and therapeutic precision. The quantity of the available imaging data and the increased developments of Machine Learning (ML), particularly Deep Learning (DL), triggered the research on uncovering "hidden" biomarkers and quantitative features from anatomical and functional medical images. Deep Neural Networks (DNN) have achieved outstanding performance and broad implementation in image processing tasks. Lately, DNNs have been considered for radiomics and their potentials for explainable AI (XAI) may help classification and prediction in clinical practice. However, most of them are using limited datasets and lack generalized applicability. In this study we review the basics of radiomics feature extraction, DNNs in image analysis, and major interpretability methods that help enable explainable AI. Furthermore, we discuss the crucial requirement of multicenter recruitment of large datasets, increasing the biomarkers variability, so as to establish the potential clinical value of radiomics and the development of robust explainable AI models.

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“…Since the advances that made it possible to learn the optical flow using CNNs (FlowNet [11]), dozens of deep-learning data-based methods have been proposed to approach the problem of deformable image registration in different clinical applications [12]. The trend is augmenting considerably in the last three years.…”

Section: Diffeomorphic Registration Constitutes the Inception Point I...mentioning

confidence: 99%

LDDMM meets GANs: Generative Adversarial Networks for diffeomorphic registration

Ramon¹,

Hernandez²,

Mayordomo³

2021

Preprint

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The purpose of this work is to contribute to the state of the art of deep-learning methods for diffeomorphic registration. We propose an adversarial learning LDDMM method for pairs of 3D mono-modal images based on Generative Adversarial Networks. The method is inspired by the recent literature for deformable image registration with adversarial learning. We combine the best performing generative, discriminative, and adversarial ingredients from the state of the art within the LD-DMM paradigm. We have successfully implemented two models with the stationary and the EPDiff-constrained non-stationary parameterizations of diffeomorphisms. Our unsupervised and data-hungry approach has shown a competitive performance with respect to a benchmark supervised and rich-data approach. In addition, our method has shown similar results to model-based methods with a computational time under one second.

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Medical image registration using deep neural networks: A comprehensive review

Cited by 135 publications

References 36 publications

Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization

Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

LDDMM meets GANs: Generative Adversarial Networks for diffeomorphic registration

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