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Automated Data Adaptation for the Segmentation of Blood Vessels

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Biomedical Engineering Systems and Technologies (BIOSTEC 2022)


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.

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The work described in this paper was supported by the Center of Excellence for Technical Innovation in Medicine (TIMED), the Dissertation Programme of the University of Applied Sciences Upper Austria, and the Austrian Research Promotion Agency (FFG, project no. 881547).

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Correspondence to Andreas Haghofer .

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Haghofer, A. et al. (2023). Automated Data Adaptation for the Segmentation of Blood Vessels. In: Roque, A.C.A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2022. Communications in Computer and Information Science, vol 1814. Springer, Cham.

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  • Print ISBN: 978-3-031-38853-8

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