Two-stage convolutional neural network for segmentation and detection of carotid web on CT angiography.
Authors
Affiliations (6)
Affiliations (6)
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.
- Department of Clinical Neurosciences, University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada.
- Diagnostic and Interventional Neuroradiology Department, University Hospital of Tours, Avenue de la République, France.
- Neurology Division, Department of Internal Medicine, University of Manitoba Max Rady College of Medicine, Winnipeg, Manitoba, Canada.
- Department of Diagnostic Imaging, Foothills Medical Center, University of Calgary, Calgary, Alberta, Canada.
- Deaprtment of Biomedical Engineering, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China [email protected].
Abstract
Carotid web (CaW) is a risk factor for ischemic stroke, mainly in young patients with stroke of undetermined etiology. Its detection is challenging, especially among non-experienced physicians. We included patients with CaW from six international trials and registries of patients with acute ischemic stroke. Identification and manual segmentations of CaW were performed by three trained radiologists. We designed a two-stage segmentation strategy based on a convolutional neural network (CNN). At the first stage, the two carotid arteries were segmented using a U-shaped CNN. At the second stage, the segmentation of the CaW was first confined to the vicinity of the carotid arteries. Then, the carotid bifurcation region was localized by the proposed carotid bifurcation localization algorithm followed by another U-shaped CNN. A volume threshold based on the derived CaW manual segmentation statistics was then used to determine whether or not CaW was present. We included 58 patients (median (IQR) age 59 (50-75) years, 60% women). The Dice similarity coefficient and 95th percentile Hausdorff distance between manually segmented CaW and the algorithm segmented CaW were 63.20±19.03% and 1.19±0.9 mm, respectively. Using a volume threshold of 5 mm<sup>3</sup>, binary classification detection metrics for CaW on a single artery were as follows: accuracy: 92.2% (95% CI 87.93% to 96.55%), precision: 94.83% (95% CI 88.68% to 100.00%), sensitivity: 90.16% (95% CI 82.16% to 96.97%), specificity: 94.55% (95% CI 88.0% to 100.0%), F1 measure: 0.9244 (95% CI 0.8679 to 0.9692), area under the curve: 0.9235 (95%CI 0.8726 to 0.9688). The proposed two-stage method enables reliable segmentation and detection of CaW from head and neck CT angiography.