Pre- And post-processing strategies for generic slice-wise segmentation of tomographic 3D datasets utilizing u-net deep learning models trained for specific diagnostic domains

Gerald A. Zwettler, Werner Backfrieder, David R. Holmes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An automated and generally applicable method for segmentation is still in focus of medical image processing research. Since a few years artificial inteligence methods show promising results, especially with widely available scalable Deep Learning libraries. In this work, a five layer hybrid U-net is developed for slice-by-slice segmentation of liver data sets. Training data is taken from the Medical Segmentation Decathlon database, providing 131 fully segmented volumes. A slice-oriented segmentation model is implemented utilizing deep learning algorithms with adaptions for variable parenchyma shape along the stacking direction and similarities between adjacent slices. Both are transformed for coronal and sagittal views. The implementation is on a GPU rack with TensorFlow and Keras. For a quantitative measure of segmentation accuracy, standardized volume and surface metrics are used. Results DSC=97.59, JI=95.29 and NSD=99.37 show proper segmentation comparable to 3D U-Nets and other state of the art. The development of a 2D-slice oriented segmentation is justified by short training time and less complexity and therefore massively reduced memory consumption. This work manifests the high potential of AI methods for general use in medical segmentation as fully- or semi-automated tool supervised by the expert user.

Original languageEnglish (US)
Title of host publicationVISAPP
EditorsGiovanni Maria Farinella, Petia Radeva, Jose Braz
PublisherSciTePress
Pages66-78
Number of pages13
ISBN (Electronic)9789897584022
StatePublished - 2020
Event15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020 - Valletta, Malta
Duration: Feb 27 2020Feb 29 2020

Publication series

NameVISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Volume5

Conference

Conference15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020
CountryMalta
CityValletta
Period2/27/202/29/20

Keywords

  • Computed Tomography
  • Deep Learning
  • Model-based Segmentation in Medicine
  • U-Net

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Pre- And post-processing strategies for generic slice-wise segmentation of tomographic 3D datasets utilizing u-net deep learning models trained for specific diagnostic domains'. Together they form a unique fingerprint.

Cite this