Zhang, XiaotongBroersen, AlexanderSokooti, HessamRamasamy, AnantharamanKitslaar, PieterParasa, RamyaDijkstra, Jouke2025-05-102025-05-1020241861-64101861-642910.1007/s11548-024-03086-22-s2.0-85187651538https://doi.org/10.1007/s11548-024-03086-2https://hdl.handle.net/20.500.14720/11032Zhang, Xiaotong/0000-0001-6085-2844PurposeThe assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fact that the range of Hounsfield units (HU) in lipid-rich areas overlaps with the HU range in fibrotic tissue and that the HU range of calcified plaques overlaps with the contrast within the contrast-filled lumen. This paper is to investigate whether lipid-rich and calcified plaques can be detected more accurately on cross-sectional CTA images using deep learning methodology.MethodsTwo deep learning (DL) approaches are proposed, a 2.5D Dense U-Net and 2.5D Mask-RCNN, which separately perform the cross-sectional plaque detection in the Cartesian and polar domain. The spread-out view is used to evaluate and show the prediction result of the plaque regions. The accuracy and F1-score are calculated on a lesion level for the DL and conventional plaque detection methods.ResultsFor the lipid-rich plaques, the median and mean values of the F1-score calculated by the two proposed DL methods on 91 lesions were approximately 6 and 3 times higher than those of the conventional method. For the calcified plaques, the F1-score of the proposed methods was comparable to those of the conventional method. The median F1-score of the Dense U-Net-based method was 3% higher than that of the conventional method.ConclusionThe two methods proposed in this paper contribute to finer cross-sectional predictions of lipid-rich and calcified plaques compared to studies focusing only on longitudinal prediction. The angular prediction performance of the proposed methods outperforms the convincing conventional method for lipid-rich plaque and is comparable for calcified plaque.eninfo:eu-repo/semantics/openAccessPlaque DetectionSpread-Out ViewCta2.5DDense U-NetMask R-CnnArticle195Q2Q297198138478204WOS:001182366400002