Authors:
Gerald A. Zwettler
1
;
2
;
Werner Backfrieder
3
and
David R. Holmes III
1
Affiliations:
1
Biomedical Analytics and Computational Engineering Lab, Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, 200 First St. SW, 55905 Rochester, MN, U.S.A.
;
2
Research Group Advanced Information Systems and Technology (AIST), Department of Software Engineering, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
;
3
Medical Informatics, Department of Software Engineering, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
Keyword(s):
Deep Learning, U-Net, Model-based Segmentation in Medicine, Computed Tomography.
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 s
tate 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.
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