Organ localization and identification in thoracic CT volumes using 3D CNNs leveraging spatial anatomic relations
Organ localization and identification in thoracic CT volumes using 3D CNNs leveraging spatial anatomic relations
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DOI：
10.1117/12.2293801
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发表时间：
2018-03
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通讯作者：
R. Soans;J. Shackleford
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作者：
R. Soans;J. Shackleford
在本文中，我们提出了一个模型，以获得先验知识的器官定位在CT胸部图像使用三维卷积神经网络（3D CNN）。具体来说，我们使用贝叶斯检测器中从CNN获得的知识来确定球坐标系中定义的给定靶器官的存在和位置。我们训练CNN来执行对可能存在于任何点x = [r，Θ，Φ]T的目标器官的软检测。该概率结果被用作贝叶斯模型中的先验，贝叶斯模型的后验概率用于为靶器官检测问题提供更准确的解决方案。贝叶斯模型的似然性通过对注释的训练体积中的器官进行空间分析来获得。在我们的案例研究中使用了来自NSCLC-Radiomics数据集的胸部CT图像，这证明了器官识别的鲁棒性和准确性的增强。在CNN阶段之后，右肺、左肺和心脏的检测器准确度的平均值分别为94.87%、95.37%和90.76%。使用贝叶斯分类器引入空间关系将检测器准确度分别提高到95.14%、96.20%和95.15%，显示出心脏检测的显著改善。该工作流程提高了检测率，因为决策是使用较低级别的特征（边缘、轮廓等）和复杂的较高级别的特征（器官之间的空间关系）来做出的。该策略还提出了一种新的应用CNN和一种新的方法，以引入更高级别的上下文特征，如图像中不同位置的对象之间的空间关系，以解决真实的世界对象检测问题。
In this paper, we present a model to obtain prior knowledge for organ localization in CT thorax images using three dimensional convolutional neural networks (3D CNNs). Specifically, we use the knowledge obtained from CNNs in a Bayesian detector to establish the presence and location of a given target organ defined within a spherical coordinate system. We train a CNN to perform a soft detection of the target organ potentially present at any point, x = [r,Θ,Φ]T. This probability outcome is used as a prior in a Bayesian model whose posterior probability serves to provide a more accurate solution to the target organ detection problem. The likelihoods for the Bayesian model are obtained by performing a spatial analysis of the organs in annotated training volumes. Thoracic CT images from the NSCLC–Radiomics dataset are used in our case study, which demonstrates the enhancement in robustness and accuracy of organ identification. The average value of the detector accuracies for the right lung, left lung, and heart were found to be 94.87%, 95.37%, and 90.76% after the CNN stage, respectively. Introduction of spatial relationship using a Bayes classifier improved the detector accuracies to 95.14%, 96.20%, and 95.15%, respectively, showing a marked improvement in heart detection. This workflow improves the detection rate since the decision is made employing both lower level features (edges, contour etc) and complex higher level features (spatial relationship between organs). This strategy also presents a new application to CNNs and a novel methodology to introduce higher level context features like spatial relationship between objects present at a different location in images to real world object detection problems.