3D fully convolutional network-based segmentation of lung nodules in CT images with a clinically inspired data synthesis method
3D fully convolutional network-based segmentation of lung nodules in CT images with a clinically inspired data synthesis method
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DOI：
10.1117/12.2511438
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发表时间：
2019-03
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影响因子：
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通讯作者：
A. Yaguchi;K. Aoyagi;A. Tanizawa;Y. Ohno
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文献类型：
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作者：
A. Yaguchi;K. Aoyagi;A. Tanizawa;Y. Ohno
在肺结节的管理中，在计算机断层扫描（CT）图像上精确评估结节大小非常重要。鉴于结节的恶性程度根据其组成而变化，成分评估对于诊断肺癌是有用的。为了提高肺结节体积测量的准确性，我们提出了一种基于深度学习的方法，用于将结节分割为多个成分，即固体，毛玻璃不透明度（GGO）和空腔。我们训练了一个3D全卷积网络（FCN），其中包含了组件级的骰子丢失，并应用了条件随机场（CRF）来细化分割边界。为了进一步获得准确性，我们根据临床观察人工生成合成空洞结节，然后增加数据集用于训练网络。在实验中使用约300个临床结节的CT图像，我们评估了我们的方法的体积测量的平均绝对百分比误差。我们证实，我们的方法实现了15.84%的低误差（平均超过2个组件的固体和GGO）相比，传统的方法基于图像处理，并与我们的数据合成方法的空腔的误差减少了2.87%。
In the management of lung nodules, it is important to precisely assess nodule size on computed tomography (CT) images. Given that the malignancy of nodules varies according to their composition, component-wise assessment is useful for diagnosing lung cancer. To improve the accuracy of volumetric measurement of lung nodules, we propose a deep learning-based method for segmenting nodules into multiple components, namely, solid, ground glass opacity (GGO), and cavity. We train a 3D fully convolutional network (FCN) with component-wise dice loss and apply a conditional random field (CRF) to refine the segmentation boundaries. To further gain the accuracy, we artificially generate synthetic cavitary nodules based on clinical observations and then augment the dataset for training the network. In experiments using about 300 CT images of clinical nodules, we evaluated our method in terms of mean absolute percentage error of volumetric measurement. We confirmed that our method achieved 15.84% lower error (averaged over 2 components of solid and GGO) compared with a conventional method based on image processing, and the error for cavity was decreased by 2.87% with our data-synthesis method.