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

机译：使用k空间数据扩充和课程学习基于CNN的心脏MR运动伪影自动检测

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摘要

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.
机译：高质量的医学图像是后续图像分析管道成功的前提。因此，医学图像的质量评估是一项必不可少的活动，对于诸如UK Biobank（UKBB）之类的大量人群研究，人工识别人工制品（例如由意外运动引起的人工制品）既繁琐又耗时。因此，迫切需要自动图像质量评估技术。在本文中，我们提出了一种自动检测心脏磁共振（CMR）电影图像中与运动有关的伪影的方法。我们比较了两种深度学习架构以对质量较差的CMR图像进行分类：1）3D时空卷积神经网络（3D-CNN），2）长期递归卷积网络（LRCN）。尽管在实际的临床环境中运动伪影很常见，但UKBB的高质量成像（包括不一定有健康问题的志愿者的横断面数据）会产生高度失衡的分类问题。由于与具有运动伪像的图像相对较少的数量相比，高质量图像的数量较高，因此我们提出了一种基于k空间中合成伪像创建的新颖数据增强方案。我们还研究了一种基于合成人工制品严重性的预定课程学习方法。我们在由3510个CMR图像组成的UK Biobank数据集的子集上评估我们的管道。 LRCN架构优于3D-CNN架构，并且能够在不到1ms的时间内以较高的召回率检测出具有运动伪影的2D +时间短轴图像。我们将我们的方法与一系列最先进的质量评估方法进行了比较。新颖的数据扩充和课程学习方法均提高了分类性能，在ROC曲线为0.89的情况下实现了总面积的增加。

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