AngioUnet - A Convolutional Neural Network for Vessel Segmentation in Cerebral DSA Series

Christian Neumann, Klaus-Dietz Tönnies, Regina Pohle-Fröhlich

2018

Abstract

The U-net is a promising architecture for medical segmentation problems. In this paper, we show how this architecture can be effectively applied to cerebral DSA series. The usage of multiple images as input allows for better distinguishing between vessel and background. Furthermore, the U-net can be trained with a small corpus when combined with useful data augmentations like mirroring, rotation, and additionally biasing. Our variant of the network achieves a DSC of 87.98% on the segmentation task. We compare this to different configurations and discuss the effect on various artifacts like bones, glue, and screws.

Paper Citation

in Harvard Style

Neumann C., Tönnies K. and Pohle-Fröhlich R. (2018). AngioUnet - A Convolutional Neural Network for Vessel Segmentation in Cerebral DSA Series. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP; ISBN 978-989-758-290-5, SciTePress, pages 331-338. DOI: 10.5220/0006570603310338

in Bibtex Style

@conference{visapp18,
author={Christian Neumann and Klaus-Dietz Tönnies and Regina Pohle-Fröhlich},
title={AngioUnet - A Convolutional Neural Network for Vessel Segmentation in Cerebral DSA Series},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP},
year={2018},
pages={331-338},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006570603310338},
isbn={978-989-758-290-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP
TI - AngioUnet - A Convolutional Neural Network for Vessel Segmentation in Cerebral DSA Series
SN - 978-989-758-290-5
AU - Neumann C.
AU - Tönnies K.
AU - Pohle-Fröhlich R.
PY - 2018
SP - 331
EP - 338
DO - 10.5220/0006570603310338
PB - SciTePress