AUTOMATIC RENAL SEGMENTATION IN DCE-MRI USING CONVOLUTIONAL NEURAL NETWORKS.
AUTOMATIC RENAL SEGMENTATION IN DCE-MRI USING CONVOLUTIONAL NEURAL NETWORKS.
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DOI：
10.1109/isbi.2018.8363865
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Kurugol S
中科院分区：
文献类型：
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作者：
Haghighi M;Warfield SK;Kurugol S
应用动态增强MRI（DCE-MRI）评价肾功能有助于儿童肾脏疾病的诊断和治疗。肾实质的自动分割是这一过程中的重要一步。在本文中，我们提出了一种时间和内存高效的全自动分割方法，实现了高分割精度的运行时间在正常肾脏和肾脏与肾积水的顺序为秒。所提出的方法是基于两个3D卷积神经网络的级联应用，其同时采用空间和时间信息，以便分别学习肾脏的定位和分割任务。分割性能进行评估，正常和异常的肾脏与不同程度的肾积水。我们实现了平均骰子系数为91.4和83.6正常和异常的肾脏的儿科患者，分别。
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis. The proposed method is based on a cascaded application of two 3D convolutional neural networks that employs spatial and temporal information at the same time in order to learn the tasks of localization and segmentation of kidneys, respectively. Segmentation performance is evaluated on both normal and abnormal kidneys with varying levels of hydronephrosis. We achieved a mean dice coefficient of 91.4 and 83.6 for normal and abnormal kidneys of pediatric patients, respectively.