Authors:
Christian Neumann
1
;
Klaus-Dietz Tönnies
2
and
Regina Pohle-Fröhlich
1
Affiliations:
1
Hochschule Niederrhein University of Applied Sciences, Germany
;
2
Otto-von-Guericke University of Magdeburg, Germany
Keyword(s):
CNN, Cerebral, DSA Series, Vessel Segmentation.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Medical Image Applications
;
Segmentation and Grouping
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.