Automated Data Adaptation for the Segmentation of Blood Vessels

Andreas Haghofer, Thomas Ebner, Philipp Kainz, Michael Weißensteiner, Nassim Ghaffari-Tabrizi-Wizsy, Isra Hatab, Josef Scharinger, Stephan Winkler

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

In the field of image analysis used for diagnostic processes, domain shifts constitute a significant obstacle. Domain shifts lead to an incompatibility of an otherwise well-performing AI model for image segmentation. Accordingly, if two different machines image the same tissue, the model may provide better results for one of the two images depending on the similarity of the image data compared to the training data for generating the AI model. In this paper, we analyzed how the input images of a neural network have to be adapted to provide better segmentation results for images which are previously not compatible with the used model. Therefore, we developed two approaches to increase a model’s segmentation quality for a dataset with initially poor results. The first approach is based on heuristic optimization and creates a set of image processing algorithms for the data adaptation. Our algorithm selects the best combination of algorithms and generates the most suitable parameters for them regarding the resulting segmentation quality. The second approach uses an additional neural network for learning the incompatible dataset’s recoloring based on the resulting segmentation quality. Both methods increase the segmentation quality significantly without the need for changes to the segmentation model itself.

Original languageEnglish
Title of host publicationBiomedical Engineering Systems and Technologies - 15th International Joint Conference, BIOSTEC 2022, Revised Selected Papers
EditorsAna Cecília A. Roque, Denis Gracanin, Ronny Lorenz, Athanasios Tsanas, Nathalie Bier, Ana Fred, Hugo Gamboa
PublisherSpringer
Pages53-72
Number of pages20
ISBN (Print)9783031388538
DOIs
Publication statusPublished - 2023
EventProceedings of the 15th International Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022 - Virtual, Online
Duration: 9 Feb 202211 Feb 2022

Publication series

NameCommunications in Computer and Information Science
Volume1814 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

ConferenceProceedings of the 15th International Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022
CityVirtual, Online
Period09.02.202211.02.2022

Keywords

  • Heuristic optimization
  • Image processing
  • Machine learning

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