Deep Learning Using K-Space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

Öksüz, İlkay; Ruijsink, Bram; Puyol‐Antón, Esther; Bustin, Aurélien; Cruz, Gastão; Prieto, Claudia; Rueckert, Daniel; Schnabel, Julia A.; King, Andrew P.

doi:10.1007/978-3-030-00928-1_29

Cited by 17 publications

(10 citation statements)

References 15 publications

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“…This paper builds upon our previous work (Oksuz et al, 2018), in which we proposed the use of synthetically generated mistriggering artefacts in training a CNN. Here, we extend this idea to include both breathing and mistriggering artefacts and also use different levels of corruption to enable the curriculum learning strategy to be introduced.…”

Section: Related Workmentioning

confidence: 93%

“…An analysis of multiple deep learning architectures and learning mechanisms is also presented. This paper builds upon our previously presented work (Oksuz et al, 2018), in which we proposed the use of synthetically generated mistriggering artefacts in training a Convolutional Neural Network (CNN). Here, we extend this idea to include both breathing and mistriggering artefacts and also use different levels of corruption in order to produce a curriculum of realistic artefact images of varying severity (Fig.…”

Section: Introductionmentioning

confidence: 96%

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Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

Öksüz

Ruijsink

Puyol‐Antón

et al. 2019

Medical Image Analysis

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Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89.

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Section: Related Workmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 96%

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

Öksüz

Ruijsink

Puyol‐Antón

et al. 2019

Medical Image Analysis

Self Cite

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“…For MRI, augmenting images with motion artefacts is possible by manipulating the k‐space. When the image is then reconstructed from this space motion artefacts will be visible, which is useful for training a model to detect or correct these artefacts or to train a model which is more robust under these conditions 167–169 . Liu et al 170 .…”

Section: Methodsmentioning

confidence: 99%

A review of medical image data augmentation techniques for deep learning applications

et al. 2021

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Summary Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning‐based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren’t typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state‐of‐the‐art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

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“…For the anatomical region of eye, Li et al [ For the detection of artefacts in Cardiac Magnetic Resonance (CMR) imaging, Oksuz et al [82] also proposed a CNN based technique. Before training the model, they performed image pre-processing by normalization and region of interest (ROI) extraction.…”

Section: Eyementioning

confidence: 99%

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

et al. 2019

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Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in 'Medical Imaging with Deep Learning' in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out 'lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future.

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Deep Learning Using K-Space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

Cited by 17 publications

References 15 publications

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

A review of medical image data augmentation techniques for deep learning applications

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

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