A supervised learning approach for diffusion MRI quality control with minimal training data
A supervised learning approach for diffusion MRI quality control with minimal training data
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DOI：
10.1016/j.neuroimage.2018.05.077
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发表时间：
2018-09-01
期刊：
影响因子：
5.7
通讯作者：
Zhang, Hui
中科院分区：
文献类型：
--
作者：
Graham, Mark S.;Drobnjak, Ivana;Zhang, Hui
质量控制（QC）是任何研究的基本组成部分。扩散MRI具有独特的挑战，使得手动QC特别困难，包括比其他MR模态更多的伪影和更大的数据量。黄金标准是手动检查数据，但这个过程既耗时又主观。最近，基于卷积神经网络的监督学习方法已被证明与人工检查具有竞争力。这些方法的缺点是它们仍然需要手动标记的数据集进行训练，这本身是耗时的，并且仍然引入了主观性的元素。在这项工作中，我们证明了手动标记的需要可以大大减少模拟数据的训练，并使用少量的标记数据的最终校准步骤。我们证明了其潜在的检测严重的运动伪影，并比较性能的分类训练手动标记的真实的数据。
Quality control (QC) is a fundamental component of any study. Diffusion MRI has unique challenges that make manual QC particularly difficult, including a greater number of artefacts than other MR modalities and a greater volume of data. The gold standard is manual inspection of the data, but this process is time-consuming and subjective. Recently supervised learning approaches based on convolutional neural networks have been shown to be competitive with manual inspection. A drawback of these approaches is they still require a manually labelled dataset for training, which is itself time-consuming to produce and still introduces an element of subjectivity. In this work we demonstrate the need for manual labelling can be greatly reduced by training on simulated data, and using a small amount of labelled data for a final calibration step. We demonstrate its potential for the detection of severe movement artefacts, and compare performance to a classifier trained on manually-labelled real data.