KEYWORDS: Transformers, Heart, Education and training, Computed tomography, Atherosclerosis, Angiography, Network architectures, Deep learning, Medicine, Medical research
Background: compare the performance of 2 novel deep learning networks—convolutional long short-term memory and transformer network—for artificial intelligence-based quantification of plaque volume and stenosis severity from CCTA. Methods: This was an international multicenter study of patients undergoing CCTA at 11 sites. The deep learning (DL) convolutional neural networks were trained to segment coronary plaque in 921 patients (5,045 lesions). The training dataset was further split temporally into training (80%) and internal validation (20%) datasets. The primary DL architecture was a hierarchical convolutional long short- term memory (ConvLSTM) network. This was compared against a TransUNet network, which combines the abilities of Vision Transformer with U-Net, enabling the capture of in-depth localization information while modeling long-range dependencies. Following training and internal validation, the both DL networks were applied to an external validation cohort of 162 patients (1,468 lesions) from the SCOT-HEART trial. Results: In the external validation cohort, agreement between DL and expert reader measurements was stronger when using the ConvLSTM network than with TransUNet, for both per-lesion total plaque volume (ICC 0·953 vs 0.830) and percent diameter stenosis (ICC 0·882 vs 0.735; both p<0.001). The ConvLSTM network showed higher per-cross-section overlap with expert reader segmentations (as measured by the Dice coefficient) compared to TransUnet, for vessel wall (0.947 vs 0.946), lumen (0.93 vs 0.92), and calcified plaque (0.87 vs 0.86; p<0.0001 for all), with similar execution times. Conclusions: In a direct comparison with external validation, the ConvLSTM network yielded higher agreement with expert readers for quantification of total plaque volume and stenosis severity compared to TransUnet, with faster execution times.
Coronary artery calcium (CAC) scores are a well-established marker of the extent of coronary atherosclerosis. We aimed to compare state-of-the-art vision transformer for medical image segmentation with convolutional long short-term memory (convLSTM) networks for automatic CAC quantification with external validation.
Background: Coronary computed tomography angiography (CCTA) allows non-invasive assessment of luminal stenosis and coronary atherosclerotic plaque. We aimed to develop and externally validate an artificial intelligence-based deep learning (DL) network for CCTA-based measures of plaque volume and stenosis severity. Methods: This was an international multicenter study of 1,183 patients undergoing CCTA at 11 sites. A novel DL convolutional neural network was trained to segment coronary plaque in 921 patients (5,045 lesions). The DL architecture consisted of a novel hierarchical convolutional long short-term memory (ConvLSTM) Network. The training set was further split temporally into training (80%) and internal validation (20%) datasets. Each coronary lesion was assessed in a 3D slab about the vessel centrelines. Following training and internal validation, the model was applied to an independent test set of 262 patients (1,469 lesions), which included an external validation cohort of 162 patients Results: In the test set, there was excellent agreement between DL and clinician expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0.964) and percent diameter stenosis (ICC 0.879; both p<0.001, see tables and figure). The average per-patient DL plaque analysis time was 5.7 seconds versus 25-30 minutes taken by experts. There was significantly higher overlap measured by the Dice coefficient (DC) for ConvLSTM compared to UNet (DC for vessel 0.94 vs 0.83, p<0.0001; DC for lumen and plaque 0.90 vs 0.83, p<0.0001) or DeepLabv3 (DC for vessel both 0.94; DC for lumen and plaque 0.89 vs 0.84, p<0.0001). Conclusions: A novel externally validated artificial intelligence-based network provides rapid measurements of plaque volume and stenosis severity from CCTA which agree closely with clinician expert readers.
We aimed to develop a novel deep-learning based method for automatic coronary artery calcium (CAC) quantification in low-dose ungated computed tomography attenuation correction maps (CTAC). In this study, we used convolutional long-short -term memory deep neural network (conv-LSTM) to automatically derive coronary artery calcium score (CAC) from both standard CAC scans and low-dose ungated scans (CT-attenuation correction maps). We trained convLSTM to segment CAC using 9543 scans. A U-Net model was trained as a reference method. Both models were validated in the OrCaCs dataset (n=32) and in the held-out cohort (n=507) without prior coronary interventions who had CTAC standard CAC scan acquired contemporarily. Cohen’s kappa coefficients and concordance matrices were used to assess agreement in four CAC score categories (very low: <10, low:10-100; moderate:101-400 and high <400). The median time to derive results on a central processing unit (CPU) was significantly shorter for the conv-LSTM model- 6.18s (inter quartile range [IQR]: 5.99, 6.3) than for UNet (10.1s, IQR: 9.82, 15.9s, p<0.0001). The memory consumption during training was much lower for our model (13.11Gb) in comparison with UNet (22.31 Gb). Conv-LSTM performed comparably to UNet in terms of agreement with expert annotations, but with significantly shorter inference times and lower memory consumption
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