@article {Groves2020,
	title = {Automatic segmentation of the carotid artery and internal jugular vein from 2D ultrasound images for 3D vascular reconstruction},
	journal = {International Journal of Computer Assisted Radiology and Surgery},
	number = {Cvc},
	year = {2020},
	publisher = {Springer International Publishing},
	abstract = {Purpose: In the context of analyzing neck vascular morphology, this work formulates and compares Mask R-CNN and U-Net-based algorithms to automatically segment the carotid artery (CA) and internal jugular vein (IJV) from transverse neck ultrasound (US). Methods: US scans of the neck vasculature were collected to produce a dataset of 2439 images and their respective manual segmentations. Fourfold cross-validation was employed to train and evaluate Mask RCNN and U-Net models. The U-Net algorithm includes a post-processing step that selects the largest connected segmentation for each class. A Mask R-CNN-based vascular reconstruction pipeline was validated by performing a surface-to-surface distance comparison between US and CT reconstructions from the same patient. Results: The average CA and IJV Dice scores produced by the Mask R-CNN across the evaluation data from all four sets were 0.90 {\textpm} 0.08 and 0.88 {\textpm} 0.14. The average Dice scores produced by the post-processed U-Net were 0.81 {\textpm} 0.21 and 0.71 {\textpm} 0.23 , for the CA and IJV, respectively. The reconstruction algorithm utilizing the Mask R-CNN was capable of producing accurate 3D reconstructions with majority of US reconstruction surface points being within 2~mm of the CT equivalent. Conclusions: On average, the Mask R-CNN produced more accurate vascular segmentations compared to U-Net. The Mask R-CNN models were used to produce 3D reconstructed vasculature with a similar accuracy to that of a manually segmented CT scan. This implementation of the Mask R-CNN network enables automatic analysis of the neck vasculature and facilitates 3D vascular reconstruction.},
	keywords = {Automatic segmentation, deep learning, Surface reconstruction, Surgical guidance, US, Vasculature},
	issn = {18616429},
	doi = {10.1007/s11548-020-02248-2},
	url = {https://doi.org/10.1007/s11548-020-02248-2},
	author = {Groves, Leah A. and VanBerlo, Blake and Veinberg, Natan and Alboog, Abdulrahman and Peters, Terry M. and Chen, Elvis C.S.}
}
