White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks
White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks
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DOI：
10.1016/j.nicl.2017.12.022
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发表时间：
2018-01-01
影响因子：
4.2
通讯作者：
Rueckert, D.
中科院分区：
文献类型：
--
作者：
Guerrero, R.;Qin, C.;Rueckert, D.
白色高信号（WMH）是散发性小血管疾病的特征，也经常在健康老年受试者的磁共振成像（MRI）中观察到。WMH负担的准确评估对于流行病学研究至关重要，以确定WMH，认知和临床数据之间的关联;其原因以及随机试验中新治疗方法的效果。WMH的手动描绘是非常繁琐、昂贵且耗时的过程，其需要由专家注释者（例如，受过训练的图像分析师或放射科医师）来执行。由于其他病理特征（即卒中病变）通常也表现为高信号区域，因此WMH描绘的问题进一步复杂化。最近，已经提出了几种旨在解决WMH分割挑战的自动化方法。这些方法中的大多数已经专门开发用于在MRI中分割WMH，但不能区分WMH和中风。能够区分脑MRI中不同病理的其他方法并未考虑同时WMH和中风分割。因此，尚未完全确定可以分割和区分MRI上这两种病理表现的任务特定的、可靠的、全自动的方法。在这项工作中，我们建议使用卷积神经网络（CNN），它能够分割高信号并区分WMH和中风病变。具体来说，我们的目标是区分WMH病理与那些由于皮质、大或小皮质下梗死引起的卒中病变。所提出的全卷积CNN架构称为uResNet，它包括一个分析路径，该路径逐渐学习低级和高级特征，然后是一个合成路径，该路径逐渐将低级和高级特征组合并上采样为类似然语义分割。量化，所提出的CNN架构被证明在与手动专家注释的重叠方面优于其他成熟的和最先进的算法。临床上，提取的WMH卷被发现与Fazekas视觉评分比竞争的方法或专家注释的卷更好地相关。此外，临床风险因素与所提出的方法生成的WMH体积之间的关联比较发现与专家注释体积的关联一致。
White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data; their causes, and the effects of new treatments in randomized trials. The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e.g. a trained image analyst or radiologist). The problem of WMH delineation is further complicated by the fact that other pathological features (i.e. stroke lesions) often also appear as hyperintense regions. Recently, several automated methods aiming to tackle the challenges of WMH segmentation have been proposed. Most of these methods have been specifically developed to segment WMH in MRI but cannot differentiate between WMHs and strokes. Other methods, capable of distinguishing between different pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. Therefore, a task specific, reliable, fully automated method that can segment and differentiate between these two pathological manifestations on MRI has not yet been fully identified. In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and differentiate between WMHs and stroke lesions. Specifically, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. The proposed fully convolutional CNN architecture, called uResNet, that comprised an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN architecture is shown to outperform other well established and state-of-the-art algorithms in terms of overlap with manual expert annotations. Clinically, the extracted WMH volumes were found to correlate better with the Fazekas visual rating score than competing methods or the expert-annotated volumes. Additionally, a comparison of the associations found between clinical risk-factors and the WMH volumes generated by the proposed method, was found to be in line with the associations found with the expert-annotated volumes.