Purpose

Deep learning can be used for improving the performance of computer-aided detection (CADe) in various medical imaging tasks. However, in computed tomographic (CT) colonography, the performance is limited by the relatively small size and the variety of the available training datasets. Our purpose in this study was to develop and evaluate a flow-based generative model for performing 3D data augmentation of colorectal polyps for effective training of deep learning in CADe for CT colonography.

Methods

We developed a 3D-convolutional neural network (3D CNN) based on a flow-based generative model (3D Glow) for generating synthetic volumes of interest (VOIs) that has characteristics similar to those of the VOIs of its training dataset. The 3D Glow was trained to generate synthetic VOIs of polyps by use of our clinical CT colonography case collection. The evaluation was performed by use of a human observer study with three observers and by use of a CADe-based polyp classification study with a 3D DenseNet.

Results

The area-under-the-curve values of the receiver operating characteristic analysis of the three observers were not statistically significantly different in distinguishing between real polyps and synthetic polyps. When trained with data augmentation by 3D Glow, the 3D DenseNet yielded a statistically significantly higher polyp classification performance than when it was trained with alternative augmentation methods.

Conclusion

The 3D Glow-generated synthetic polyps are visually indistinguishable from real colorectal polyps. Their application to data augmentation can substantially improve the performance of 3D CNNs in CADe for CT colonography. Thus, 3D Glow is a promising method for improving the performance of deep learning in CADe for CT colonography.

中文翻译：

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用于计算机断层扫描结肠造影的 3D 深度学习中体积数据增强的基于生成流的模型

目的

深度学习可用于提高计算机辅助检测 (CADe) 在各种医学成像任务中的性能。然而，在计算机断层扫描 (CT) 结肠成像中，性能受到相对较小的尺寸和可用训练数据集的多样性的限制。我们在本研究中的目的是开发和评估一种基于流的生成模型，用于执行结直肠息肉的 3D 数据增强，以有效训练用于 CT 结肠造影的 CADe 深度学习。

方法

我们基于基于流的生成模型 (3D Glow) 开发了一个 3D 卷积神经网络 (3D CNN)，用于生成具有与其训练数据集的 VOI 相似的特征的合成感兴趣体积 (VOI)。通过使用我们的临床 CT 结肠造影病例集，对 3D Glow 进行训练以生成合成的息肉 VOI。评估是通过使用三名观察者的人类观察者研究和使用基于 CADe 的息肉分类研究和 3D DenseNet 进行的。

结果

三名观察者的接受者操作特征分析的曲线下面积值在区分真实息肉和合成息肉方面没有统计学上的显着差异。当使用 3D Glow 进行数据增强训练时，3D DenseNet 产生的息肉分类性能在统计学上显着高于使用其他增强方法训练时的性能。

结论

3D Glow 生成的合成息肉在视觉上与真正的结直肠息肉无法区分。它们在数据增强中的应用可以显着提高 3D CNN 在 CADe 中用于 CT 结肠造影的性能。因此，3D Glow 是一种很有前途的方法，可以提高 CADe 中用于 CT 结肠造影的深度学习性能。