Automatic segmentation of the carotid artery and internal jugular vein from 2D ultrasound images for 3D vascular reconstruction

• DOI: 10.1007/s11548-020-02248-2
• Publication Date: 2020-08-24T17:02:27Z
• Authors: Leah A. Groves; Blake VanBerlo; Natan Veinberg; Abdulrahman Alboog; Terry M. Peters; Elvis C. S. Chen
• Keywords: US; 610; 600; Deep learning; 03 medical and health sciences; Carotid Arteries; Deep Learning; 0302 clinical medicine; Image Processing, Computer-Assisted; Vasculature; Humans; Automatic segmentation; Jugular Veins; Surface reconstruction; Surgical guidance; Algorithms; Ultrasonography

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).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.The average CA and IJV Dice scores produced by the Mask R-CNN across the evaluation data from all four sets were [Formula: see text] and [Formula: see text]. The average Dice scores produced by the post-processed U-Net were [Formula: see text] and [Formula: see text], 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.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.