This paper proposes a new fish bone detection technique using a convolutional neural network (CNN) and synthetic image generation. Semantic segmentation CNNs with supervised learning generally require a large number of training images and their pixel-wise teaching signals. In fish bone detection, there are two problems with using semantic segmentation CNNs. One is the manual annotations of fish bones and the other is the difficulty of sampling all variations of fish bones with various lengths, angles, and thicknesses. The proposed method, however, generates them by drawing virtual fish bones on X-ray images. This technique is very useful for reducing the cost of collecting and annotating a dataset. Experimental results have shown that the average F-measure for the proposed method is 0.747, while that for a normal training method is 0.493. In the proposed method, the CNN successfully detected actual fish bones despite its training only with virtual fish bones. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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使用卷积神经网络和合成图像生成在 X 射线图像中自动检测鱼骨

本文提出了一种使用卷积神经网络 (CNN) 和合成图像生成的新鱼骨检测技术。具有监督学习的语义分割 CNN 通常需要大量训练图像及其像素级教学信号。在鱼骨检测中，使用语义分割 CNN 存在两个问题。一个是鱼骨的人工标注，另一个是很难对不同长度、角度和厚度的鱼骨的所有变化进行采样。然而，所提出的方法是通过在 X 射线图像上绘制虚拟鱼骨来生成它们的。这种技术对于降低收集和注释数据集的成本非常有用。实验结果表明，平均F所提出方法的测量值为 0.747，而正常训练方法的测量值为 0.493。在所提出的方法中，尽管 CNN 仅使用虚拟鱼骨进行训练，但它还是成功地检测到了实际的鱼骨。© 2021 日本电气工程师学会。由 Wiley Periodicals LLC 出版。