Table 1.

A summary of related work in the literature on cerebral vascular segmentation. The geometric feature extraction column indicates whether the paper presented any geometric features of the vasculature and the skeletonization column indicates whether this method obtains the centerline and diameter information needed for CFD and mesh reconstruction. The last two columns specify the corresponding validation protocol and the major limitations which we tried to address in our method.

Authors	Method	Modality		Skeletonization	Geometric Feature Extraction	Validation Protocol	Major Limitations
Flasque et al. (2001)	Centerline tracking and modeling	MRA	Manual or Semi-Automatic	✓	×	Phantom	Manual intervention required
Passat et al. (2006)	ATLAS registration with anatomical modeling and hit-or-miss transform	PC-MRA		✓	×	Manual	Manual intervention required
Chen et al. (2018b)	Semi-automated Open-Curve Active Contour Vessel Tracing	3D MRA		✓	✓	Manual	Some manual intervention required, only tested on patients with intracranial arterial stenosis
Gao et al. (2012)	Statistical model analysis and curve evaluation	MRA		×	×	Manual	Intensity based statistical analysis and local curve evaluation resulting in under-segmentation
Wright et al. (2013)	Neuron_Morpho plugin in ImageJ for segmentation (discontinued), morphometric analysis and feature extraction	MRA		✓	✓	NA	Insufficient Validation, performance accuracy unclear
Hsu et al.(2017)	Multiscale composite filter and mesh generation	MRA	Fully Automatic	✓	Limited	Manual, phantom	Not tested on CT data, limited feature extraction
Wang et al. (2015)	Otsu and Gumbel distribution-based threshold	MRA		×	×	Manual	Misclassification of skull pixels, under- segmentation of small vessels
Chen et al. (2018a)	Deep learning 3D U-Net architecture without manual annotation	MRA (CTA for training data)		×	×	Manual	Thresholding based filtering to generate training data, insufficient validation
Meijs et al. (2017)	Random forest classifier with local histogram features	4D CT		×	×	Manual	No geometrical information, manual validation
Zhao et al. (2018)	Weighted Symmetry Filter	MRA, Retinal images		×	×	Manual, phantom	No skeleton or geometrical information
Livne et al. (2019)	Deep learning-based U-net architecture	MRA		×	×	Manual	Poor inter-modal performance (monocentric data), no skeleton or geometrical information, no healthy dataset