Abstract Body: Background: Rapid, accurate diagnosis and characterization of carotid atherosclerosis can help prevent disabling strokes. Although carotid plaques can be identified on CT angiography (CTA), interpretation is challenging for frontline physicians. Quantification of plaque volume/composition requires much manual effort. We developed and tested a fully automated tool for segmenting carotid lumens and delineating atherosclerotic plaque, distinguishing between calcific and hypodense components.

Methods: We used 528 consecutive cases with CTA head/neck from a population of 7,745 patients with ischemic stroke and transient ischemic attack in an entire province (Alberta) presenting from 1-April-2016 to 31-March-2017. Trained readers supervised by a radiologist manually segmented right and left carotid artery lumens from 3 vertebral bodies below the bulb to 3 above, and segmented regions of carotid atherosclerotic plaque, regardless of degree of stenosis, separately labelling calcific and hypodense components. Cases were split 80/20 between training and testing datasets. The fully automated pipeline included coarse-scale detection of regions of interest, a two-stream U-shaped network for detection and segmentation of lumens and plaques, and a geometry-based inference algorithm to distinguish left and right labels. We evaluated plaque detection using diagnostic performance measures and segmentation using the Dice coefficient.

Results: 422 cases were used for training and 106 for testing. In testing data, the fully automated tool achieved excellent performance for bilateral segmentation of carotid lumens (Dice 0.91, 95%CI:0.90-0.92, e.g. Figure 1). For detection of calcific and hypodense plaque components, respectively, the model achieved sensitivity of 96.5% (95%CI:89.3-99.1%) and 97.3% (89.6-99.5%), specificity of 95.2% (74.1-99.8%) and 75.8% (57.4-88.3%), positive predictive value of 98.8% (92.6-99.9%) and 89.9% (80.5-95.2%), negative predictive value of 87.0% (65.3-96.6%) and 92.6% (74.2-98.7%), and accuracy of 96.2% (90.1-98.8%) and 90.6% (82.9-95.1%, e.g. Figure 2). Dice score was 0.73 (0.69-0.76) for calcific plaque and 0.53 (0.49-0.57) for hypodense plaque.

Conclusions: Our fully automated tool achieved good performance for detection and segmentation of carotid plaques. Calcific and hypodense plaque volumes can be automatically generated from these labels. Efforts are underway to further optimize the specificity and segmentation performance for hypodense plaques.