Understanding and interpreting machine learning in medical image computing applications : first international workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018 :...最新文献

英文中文

Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks. 基于深度卷积网络的脑室分割存在的不足。

Understanding and interpreting machine learning in medical image computing applications : first international workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018 :...

Pub Date : 2018-09-01 Epub Date: 2018-10-24 DOI: 10.1007/978-3-030-02628-8_9

Muhan Shao, Shuo Han, Aaron Carass, Xiang Li, Ari M Blitz, Jerry L Prince, Lotta M Ellingsen

Normal Pressure Hydrocephalus (NPH) is a brain disorder that can present with ventriculomegaly and dementia-like symptoms, which often can be reversed through surgery. Having accurate segmentation of the ventricular system into its sub-compartments from magnetic resonance images (MRI) would be beneficial to better characterize the condition of NPH patients. Previous segmentation algorithms need long processing time and often fail to accurately segment severely enlarged ventricles in NPH patients. Recently, deep convolutional neural network (CNN) methods have been reported to have fast and accurate performance on medical image segmentation tasks. In this paper, we present a 3D U-net CNN-based network to segment the ventricular system in MRI. We trained three networks on different data sets and compared their performances. The networks trained on healthy controls (HC) failed in patients with NPH pathology, even in patients with normal appearing ventricles. The network trained on images from HC and NPH patients provided superior performance against state-of-the-art methods when evaluated on images from both data sets.

常压脑积水(NPH)是一种脑部疾病，可表现为脑室肿大和痴呆样症状，通常可通过手术逆转。从磁共振图像(MRI)中将心室系统精确分割为其亚室将有助于更好地表征NPH患者的病情。以往的分割算法需要较长的处理时间，往往不能准确分割NPH患者严重扩大的心室。近年来，深度卷积神经网络(CNN)方法在医学图像分割任务中具有快速、准确的性能。在本文中，我们提出了一种基于cnn的三维U-net网络在MRI中分割心室系统。我们在不同的数据集上训练了三个网络，并比较了它们的性能。在健康对照(HC)上训练的神经网络在NPH病理患者中失败，即使在心室外观正常的患者中也是如此。当对来自HC和NPH患者的图像进行评估时，对来自这两个数据集的图像进行训练的网络提供了优于最先进方法的性能。

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