Ronneberger, O., Fischer, P. and Brox, T. (2014) 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, Springer, Cham, 234-241.
https://doi.org/10.1007/978-3-319-24574-4_28

has been cited by the following article:

• TITLE: Comparative Study of Segmentation Methods of Rat Sinusoidal Network to Evaluate Nonalcoholic Steatohepatitis for Small Number of Medical Samples

  AUTHORS: Masaki Murakishi, Hiroto Shoji

  KEYWORDS: Segmentation, Deep Learning, Fractal Dimensions

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.10 No.11, November 22, 2022

  ABSTRACT: Extracting portions of pathology images to determine disease states is a challenging task, and several segmentation approaches have been developed. This study evaluated four well-known methods to classify changes in rat hepatic sinusoidal network morphology in nonalcoholic stetohepatitis were evaluated. The classical methods of morphological transformation (MT), convex hull (CH), and counter extraction (CE), and a machine learning method, U-net, were compared using fractal dimensions as feature quantities. The findings show that MT and CH are more effective than U-net when the number of samples is small, as is usually the case with pathology images. U-net required a large number of images and was the best method in terms of extracting the morphology. When the number of samples is small, such as the case with pathological images, it may be advantageous to use a combination of basic segmentation methods rather than advanced machine learning techniques, such as U-net. The segmentation methods must be carefully chosen depending on the diseases to be discriminated and on the number of samples.