Detail publikace

MULTICLASS SEGMENTATION OF 3D MEDICAL DATA WITH DEEP LEARNING

SLUNSKÝ, T.

Originální název

MULTICLASS SEGMENTATION OF 3D MEDICAL DATA WITH DEEP LEARNING

Anglický název

MULTICLASS SEGMENTATION OF 3D MEDICAL DATA WITH DEEP LEARNING

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

BibTex


@proceedings{BUT164328,
  author="Tomáš {Slunský}",
  title="MULTICLASS SEGMENTATION OF 3D MEDICAL DATA WITH DEEP LEARNING",
  annote="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.",
  chapter="164328",
  howpublished="online",
  year="2020",
  month="april",
  type="conference proceedings"
}