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Using Convolutional Neural Networks in the Problem of Cell Nuclei Segmentation on Histological Images

  • Vladimir KhryashchevEmail author
  • Anton Lebedev
  • Olga Stepanova
  • Anastasiya Srednyakova
Conference paper
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 199)

Abstract

Computer-aided diagnostics of cancer pathologies based on histological image segmentation is a promising area in the field of computer vision and machine learning. To date, the successes of neural networks in image segmentation in a number of tasks are comparable to human results and can even exceed them. The paper presents a fast algorithm of histological image segmentation based on the convolutional neural network U-Net. Using this approach allows to get better results in the tasks of medical image segmentation. The developed algorithm based on neural network AlexNet was used for the creation of the automatic markup of the histological image database. The neural network algorithms were trained and tested on the NVIDIA DGX-1 supercomputer using histological images. The results of the research show that the fast algorithm based on neural network U-Net can be successfully used for the histological image segmentation in real medical practice, which is confirmed by the high level of similarity of the obtained markup with the expert one.

Keywords

Convolutional neural network Cell nuclei segmentation Histological image segmentation 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.P.G. DemidovYaroslavl State UniversityYaroslavlRussia

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