RT Journal Article
SR Electronic
T1 Two-stage convolutional neural network for segmentation and detection of carotid web on CT angiography
JF Journal of NeuroInterventional Surgery
JO J NeuroIntervent Surg
FD BMJ Publishing Group Ltd.
SP jnis-2024-021782
DO 10.1136/jnis-2024-021782
A1 Kuang, Hulin
A1 Tan, Xianzhen
A1 Bala, Fouzi
A1 Huang, Jialiang
A1 Zhang, Jianhai
A1 Alhabli, Ibrahim
A1 Benali, Faysal
A1 Singh, Nishita
A1 Ganesh, Aravind
A1 Coutts, Shelagh B
A1 Almekhlafi, Mohammed A
A1 Goyal, Mayank
A1 Hill, Michael D
A1 Qiu, Wu
A1 Menon, Bijoy K
YR 2024
UL http://jnis.bmj.com/content/early/2025/01/25/jnis-2024-021782.abstract
AB Background 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.Methods 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.Results 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 mm3, 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).Conclusions The proposed two-stage method enables reliable segmentation and detection of CaW from head and neck CT angiography.Data are available upon reasonable request.