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Ronneberger, O., Fischer, P. and Brox, T. (2015) U-Net: Convolution Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W. and Frangi, A., Eds., Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015. 18th International Conference, Munich, Germany, Proceedings, Part III.

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.