Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound
Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound
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DOI：
10.1016/j.cviu.2017.04.002
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发表时间：
2017-11-01
影响因子：
4.5
通讯作者：
Navab, Nassir
中科院分区：
文献类型：
--
作者：
Milletari, Fausto;Ahmadi, Seyed-Ahmad;Navab, Nassir
在这项工作中，我们提出了一种新的方法，通过利用卷积神经网络（CNN）的抽象能力来执行分割。我们的方法是基于霍夫投票，一种策略，允许全自动定位和分割的解剖结构的利益。这种方法不仅使用CNN分类结果，而且还通过利用网络最深部分产生的特征来实现投票。我们表明，这种基于学习的分割方法是鲁棒的，多区域的，灵活的，可以很容易地适应不同的方式。为了展示CNN应用于医学图像分析时的能力和行为，我们对六种不同网络架构的性能进行了系统研究，这些网络架构是根据最先进的标准在各种情况下构思的。我们评估了不同数量的训练数据和不同的数据维度（2D，2.5D和3D）对最终结果的影响。我们展示了分别描绘基底神经节和中脑的26个区域的MRI和经颅US体积的结果。(C)2017爱思唯尔公司All rights reserved.
In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs). Our method is based on Hough voting, a strategy that allows for fully automatic localisation and segmentation of the anatomies of interest. This approach does not only use the CNN classification outcomes, but it also implements voting by exploiting the features produced by the deepest portion of the network. We show that this learning-based segmentation method is robust, multi-region, flexible and can be easily adapted to different modalities. In the attempt to show the capabilities and the behaviour of CNNs when they are applied to medical image analysis, we perform a systematic study of the performances of six different network architectures, conceived according to state-of-the-art criteria, in various situations. We evaluate the impact of both different amount of training data and different data dimensionality (2D, 2.5D and 3D) on the final results. We show results on both MRI and transcranial US volumes depicting respectively 26 regions of the basal ganglia and the midbrain. (C) 2017 Elsevier Inc. All rights reserved.