@inproceedings{005594bc845045079ca62fedcda46fe4,
title = "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",
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.",
keywords = "Computed Tomography, Deep Learning, Model-based Segmentation in Medicine, U-Net",
author = "Zwettler, {Gerald A.} and Werner Backfrieder and Holmes, {David R.}",
note = "Publisher Copyright: Copyright {\textcopyright} 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved; 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020 ; Conference date: 27-02-2020 Through 29-02-2020",
year = "2020",
language = "English (US)",
series = "VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications",
publisher = "SciTePress",
pages = "66--78",
editor = "Farinella, {Giovanni Maria} and Petia Radeva and Jose Braz",
booktitle = "VISAPP",
}