Segmentation of anatomical structures in cardiac CTA using multi-label V-Net
Segmentation of anatomical structures in cardiac CTA using multi-label V-Net
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DOI：
10.1117/12.2293811
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发表时间：
2018-03
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通讯作者：
Hui Tang;Mehdi Moradi;A. Harouni;Hongzhi Wang;Gopalkrishna Veni;Prasanth Prasanna;T. Syeda-Mahmood
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作者：
Hui Tang;Mehdi Moradi;A. Harouni;Hongzhi Wang;Gopalkrishna Veni;Prasanth Prasanna;T. Syeda-Mahmood
分割胸部解剖结构是许多自动疾病检测应用中的关键步骤。针对该任务开发了基于多图谱的方法，然而，由于所需的可变形配准步骤，它们通常在计算上是昂贵的并且在处理时间方面产生瓶颈。相比之下，具有2D或3D内核的卷积神经网络（CNN）虽然训练速度很慢，但在部署阶段非常快，并已用于解决医学成像中的分割任务。最近报道了神经网络在医学图像分割中的性能的最新改进，即使用骰子相似系数（DSC）来优化称为V-Net的全卷积架构中的权重。然而，在以前的工作中，只有为一个前景对象计算的DSC被优化，因此基于DSC的分割CNN只能执行二进制分割。在本文中，我们将V-Net二进制架构扩展为多标签分割网络，并将其用于心脏CTA中的多个解剖结构的分割。该方法使用多标签V-Net优化的总和超过DSC的所有解剖结构，其次是后处理方法来细化分割的表面。我们的方法平均需要不到3秒分割一个完整的CTA体积。相比之下，迄今为止发布的最快的基于多图谱的方法大约需要10分钟。我们的方法使用四重交叉验证实现了16个分割解剖结构的平均DSC为76%，这接近于最先进的水平。
Segmenting anatomical structures in the chest is a crucial step in many automatic disease detection applications. Multi-atlas based methods are developed for this task, however, due to the required deformable registration step, they are often computationally expensive and create a bottle neck in terms of processing time. In contrast, convolutional neural networks (CNNs) with 2D or 3D kernels, although slow to train, are very fast in the deployment stage and have been employed to solve segmentation tasks in medical imaging. A recent improvement in performance of neural networks in medical image segmentation was recently reported when dice similarity coefficient (DSC) was used to optimize the weights in a fully convolutional architecture called V-Net. However, in the previous work, only the DSC calculated for one foreground object is optimized, as a result the DSC based segmentation CNNs are only able to perform a binary segmentation. In this paper, we extend the V-Net binary architecture to a multi-label segmentation network and use it for segmenting multiple anatomical structures in cardiac CTA. The method uses multi-label V-Net optimized by the sum over DSC for all the anatomies, followed by a post-processing method to refine the segmented surface. Our method takes averagely less than 3 sec to segment a full CTA volume. In contrast, the fastest multi-atlas based methods published so far take around 10 mins. Our method achieves an average DSC of 76% for 16 segmented anatomies using four-fold cross validation, which is close to the state-of-the-art.