Publication detail

MULTICLASS SEGMENTATION OF 3D MEDICAL DATA WITH DEEP LEARNING

SLUNSKÝ, T.

Original Title

MULTICLASS SEGMENTATION OF 3D MEDICAL DATA WITH DEEP LEARNING

Type

conference proceedings

Language

English

Original Abstract

This paper deals with multiclass image segmentation using convolutional neural networks. The theoretical part of paper focuses on image segmentation. There are basics principles of neural networks and image segmentation with more types of approaches. In practical part the Unet architecture is chosen and is described for image segmentation more. U-net was applied for medicine dataset which consist from 3D MRI of human brain. There is processing procedure which is more described for image processing of three-dimensional data. There are also methods for data preprocessing which were applied for image multiclass segmentation. Final part of paper evaluates results which were achieved with chosen method.

Keywords

deep learning, convolutional neural network, multi-class image segmentation

Authors

SLUNSKÝ, T.

Released

23. 4. 2020

ISBN

978-80-214-5942-7

Edition number

1

Pages count

5

URL

https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2020_sbornik_1.pdf

BibTex

@proceedings{BUT164328,
  editor="Tomáš {Slunský}",
  title="MULTICLASS SEGMENTATION OF 3D MEDICAL DATA WITH DEEP LEARNING",
  year="2020",
  number="1",
  pages="5",
  isbn="978-80-214-5942-7",
  url="https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2020_sbornik_1.pdf"
}