<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Groves, Leah A.</style></author><author><style face="normal" font="default" size="100%">VanBerlo, Blake</style></author><author><style face="normal" font="default" size="100%">Veinberg, Natan</style></author><author><style face="normal" font="default" size="100%">Alboog, Abdulrahman</style></author><author><style face="normal" font="default" size="100%">Peters, Terry M.</style></author><author><style face="normal" font="default" size="100%">Chen, Elvis C.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic segmentation of the carotid artery and internal jugular vein from 2D ultrasound images for 3D vascular reconstruction</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Assisted Radiology and Surgery</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Automatic segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Surface reconstruction</style></keyword><keyword><style  face="normal" font="default" size="100%">Surgical guidance</style></keyword><keyword><style  face="normal" font="default" size="100%">US</style></keyword><keyword><style  face="normal" font="default" size="100%">Vasculature</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/s11548-020-02248-2</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">Cvc</style></number><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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 ± 0.08 and 0.88 ± 0.14. The average Dice scores produced by the post-processed U-Net were 0.81 ± 0.21 and 0.71 ± 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.</style></abstract></record></records></xml>