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Chen, L., Zhu, Y., Papandreou, G., Schroff, A. and Adam, H. (2018) Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C. and Weiss, Y., Eds., Computer Vision—ECCV 2018. Lecture Notes in Computer Science, Springer, Cham.
https://doi.org/10.1007/978-3-030-01234-2_49

has been cited by the following article:

• TITLE: An Improved Neural Network Method for Forearm Bone Imaging Segmentation

  AUTHORS: Songzheng Huang, Jianfeng Chen

  KEYWORDS: Human Radius, Digital X-Ray Image, U-shaped Unet Neural Network, Segmentation

  JOURNAL NAME: Open Journal of Radiology, Vol.12 No.4, December 12, 2022

  ABSTRACT: In this paper, we propose several improved neural networks and training strategy using data augmentation to segment human radius accurately and efficiently. This method can provide pixel-level segmentation accuracy through the low-level features of the neural network, and automatically distinguish the classification of radius. The versatility and applicability can be effectively improved by learning and training digital X-ray images obtained from digital X-ray imaging systems of different manufacturers.