@inproceedings {26548,
	title = {Automated and objective segmentation of medical image using machine learning techniques: all models are wrong, but some are useful},
	journal = {Computational and Mathematical Biomedical Engineering},
	year = {2019},
	publisher = {CMBE},
	address = {Sendai, Japan},
	abstract = {Medical images are the basis of {\textquotedblright}patient-specific{\textquotedblright} simulations but come with severe limitations, most notably through operator dependencies like image segmentation. The aim was to develop an open- source pipeline for automated and objective segmentation. Combining latest advances from machine learning and signal processing, we demonstrate that the pipeline preserve all major characteristic features of a test image and identify minor branches, which can be further modified by the user. In conclusion, the default pipeline will in the majority of cases offer labor free automated and objective segmentation, or at worst provide an optimal starting point for manual segmentation.},
	author = {Per Magne {Florvaag} and Naumova, Valeriya and Valen-Sendstad, Kristian}
}
