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 |