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Moriya, T., Roth, H.R., Nakamura, S., et al. (2018) Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning. Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, International Society for Optics and Photonics, 10578, Article ID: 1057820.
https://doi.org/10.1117/12.2293414

has been cited by the following article:

• TITLE: Advances on Tumor Image Segmentation Based on Artificial Neural Network

  AUTHORS: Shaohua Wang, Jianli Jiang, Xiaobing Lu

  KEYWORDS: Artificial Neural Network, Segmentation of Tumor Image, Convolutional Neural Network

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.8 No.7, July 22, 2020

  ABSTRACT: Image technology is applied more and more to help doctors to improve the accuracy of tumor diagnosis as well as researchers to study tumor characteristics. Image segmentation technology is an important part of image treatment. This paper summarizes the advances of image segmentation by using artificial neural network including mainly the BP network and convolutional neural network (CNN). Many CNN models with different structures have been built and successfully used in segmentation of tumor images such as supervised and unsupervised learning CNN. It is shown that the application of artificial network can improve the efficiency and accuracy of segmentation of tumor image. However, some deficiencies of image segmentation by using artificial neural network still exist. For example, new methods should be found to reduce the cost of building the marked data set. New artificial networks with higher efficiency should be built.