Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks.
Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks.
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DOI：
10.1016/j.nicl.2017.06.016
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Rueckert D
中科院分区：
文献类型：
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作者：
Chen L;Bentley P;Rueckert D
中风是一种急性脑血管疾病，很可能造成长期残疾和死亡。大多数脑卒中患者发生急性缺血性病变。这些病变在准确的诊断和治疗下是可以治疗的。尽管弥散加权磁共振成像（DWI）对这些病变很敏感，但对临床医生来说，手动定位和量化它们是昂贵的，而且具有挑战性。本文提出了一种基于DWI的脑卒中病灶自动分割框架。我们的框架由两个卷积神经网络（cnn）组成：一个是两个DeconvNets的集合，即EDD网络；第二个CNN是多尺度卷积标签评估网（MUSCLE net），其目的是评估EDD网检测到的病变，以去除潜在的假阳性。据我们所知，这是第一次尝试解决这个问题，并且使用两种cnn都取得了很好的效果。此外，我们还详细研究了网络架构和密钥配置，以确保最佳性能。在包含741名受试者临床获取的DW图像的大型数据集上进行了验证。得到的Dice系数平均精度为0.67。基于小病变和大病变受试者的平均Dice得分分别为0.61和0.83。病变检出率为0.94。一种基于深度cnn的DWI急性缺血性病变分割新框架。这是针对这一问题开发的第一个全自动方法。该算法在大型真实临床数据集上得到了验证。它取得了非常好的结果，Dice系数平均为0.67。
Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-weighted MR imaging (DWI) is sensitive to these lesions, localizing and quantifying them manually is costly and challenging for clinicians. In this paper, we propose a novel framework to automatically segment stroke lesions in DWI. Our framework consists of two convolutional neural networks (CNNs): one is an ensemble of two DeconvNets, which is the EDD Net; the second CNN is the multi-scale convolutional label evaluation net (MUSCLE Net), which aims to evaluate the lesions detected by the EDD Net in order to remove potential false positives. To the best of our knowledge, it is the first attempt to solve this problem and using both CNNs achieves very good results. Furthermore, we study the network architectures and key configurations in detail to ensure the best performance. It is validated on a large dataset comprising clinical acquired DW images from 741 subjects. A mean accuracy of Dice coefficient obtained is 0.67 in total. The mean Dice scores based on subjects with only small and large lesions are 0.61 and 0.83, respectively. The lesion detection rate achieved is 0.94. A novel framework based on deep CNNs to segment the acute ischemic lesions in DWI. It is the first fully automatic method developed for this problem. The algorithm is validated on a large real clinical dataset. It achieves very good results, which is 0.67 in terms of the Dice coefficient in average.