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25 results for “calcium score”
Data from: Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning
<p><strong>Abstract</strong></p> <p>Precise segmentation of coronary arteries in non-contrast Computed Tomography (CT) scans plays an important role in the assessment of the coronary artery disease, where it is the key component for evaluating the Calcium Score (Agatston et al. 1990). In the paper by Bujny et al. (2024), a deep-learning approach for high-precision segmentation of coronary arteries in non-contrast CT was proposed along with a novel method for generating Ground Truth (GT) test data (<em>test-GT</em>) via manual registration of high-resolution coronary tree models obtained based on contrast CT with the non-contrast CT scans. In this dataset, we present the inferences of the neural network model together with the corresponding <em>test-GT</em> samples, based on 6 CT scans from the openly available OrCaScore dataset (Wolterink et al. 2016). The geometrical models included in the dataset can be used both for inspection of the proposed deep learning model and for testing of new non-contrast coronary vessel segmentation approaches, which is a unique opportunity since, to the best of our knowledge, manual generation of GT for non-contrast coronary artery segmentation was not addressed so far due to very challenging character of this particular segmentation task.</p> <p> </p> <p><strong>Methods</strong></p> <p><strong><em>Manual Generation of test-GT</em></strong></p> <p>The geometric models of coronary arteries used for the evaluation of the proposed neural network model were generated according to the manual mesh-to-image registration process as described by Bujny et al. (2024). In this approach, the high-resolution coronary artery masks obtained based on contrast CT scans are manually aligned with the corresponding non-contrast CT images using tools available in the open-source 3D computer graphics software, Blender (<a href="https://www.blender.org/">https://www.blender.org/</a>). To ease the manual alignment process, specialized add-ons for medical image processing such as Cardiac add-on for Blender of Graylight Imaging (<a href="https://graylight-imaging.com/3d-modelling/">https://graylight-imaging.com/3d-modelling/</a>) can be used, as well. The STL models in this dataset were manually generated by a medical expert with 4 years of experience.</p> <p><strong><em>Segmentation of Coronary Arteries using a Deep Learning Model</em></strong></p> <p>For each of the cases presented in this dataset, we run an inference of an nnU-Net (Isensee et al. 2021) model trained according to the process described in our paper (Bujny et al. 2024). Since we use a standard nnU-Net, which utilizes a sliding window approach for processing of the CT scan, the context information within a patch is limited, which can lead to some false-positive detections. To mitigate this problem, we additionally post-process the inferences by eliminating small vessel fragments of less than 50 [mm^3] volume and structures outside of pericardium, which we segment using another nnU-Net model, SegTHOR (Lambert et al. 2020). The resulting geometric models are stored using the STL format and presented as green masks in the HTML reports with an embedded viewer based on the K3D-jupyter library (<a href="https://k3d-jupyter.org/">https://k3d-jupyter.org/</a>).</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>The root folder contains 6 folders whose names correspond to the CT scans from the OrCaScore dataset (Wolterink et al. 2016). In each of the folders, there are the following 4 files available:</p> <ul> <li><span>‘manualGT_rater1.stl’ – high-resolution STL model of coronary arteries obtained via manual alignment of the geometric model segmented in contrast CT with the corresponding non-contrast CT scan by the first rater.</span> A sample belonging to the <em>test-GT</em> set (Bujny et al. 2024).</li> <li>‘manualGT_rater2.stl’ – corresponding <em>test-GT</em> sample by the second rater.</li> <li>‘ML.stl’ – post-processed inference of the nnU-Net ML model in the STL format.</li> <li>‘report.html’ – interactive HTML report consisting of a manually-aligned <em>test-GT</em> sample (red mask), the ML segmentation based on the non-contrast CT scan (green mask), and selected slices of the non-contrast CT scan. The reports contain the relevant information related to the scanning device and present the main segmentation quality metrics for the ML model inference.</li> </ul>
Coronary Heart Disease as Measured by Coronary Calcium Score Among Individuals With Chronic Traumatic Spinal Cord Injury
ClinicalTrials.gov study NCT00628446. IPD Sharing: Not stated. Countries: 1. Publications: 2.
CT Calcium Scoring in Suspected Stable Angina
ClinicalTrials.gov study NCT01660594. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Predictive Value of Coronary Artery Calcium Score
ClinicalTrials.gov study NCT06311071. IPD Sharing: NO. Countries: 1. Publications: 0.
Analysis of Calcium Score of Severe Aortic Stenosis in Patients With and Without Cardiac Amyloidosis (CAUSATIVE Study)
ClinicalTrials.gov study NCT06066632. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Focused Field of View Calcium Scoring Prior to Coronary CT Angiography
ClinicalTrials.gov study NCT02972242. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Community Benefit of No-charge Calcium Score Screening Program
ClinicalTrials.gov study NCT04075162. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Effects of Physical Activity, Ambulatory Blood Pressure and Calcium Score on Cardiovascular Health in Normal People
ClinicalTrials.gov study NCT02791152. IPD Sharing: YES. Countries: 1. Publications: 2.
CRICKET Study, Coronary Calcium Scores in Patients With Chronic Kidney Disease
ClinicalTrials.gov study NCT00142636. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Use of FFR-CT in Stable Intermediate Chest Pain Patients With Severe Coronary Calcium Score
ClinicalTrials.gov study NCT03548753. IPD Sharing: NO. Countries: 1. Publications: 11.
The Diagnostic Power Of Coronary CT Angiography In Patients With Chest Pain And Zero Calcium Score
ClinicalTrials.gov study NCT06552663. IPD Sharing: Not stated. Countries: 0. Publications: 5.
Coffee Consumption and Coronary Artery Calcium Score
ClinicalTrials.gov study NCT03139071. IPD Sharing: NO. Countries: 1. Publications: 0.
ASpirin Use and stAtin Strategy for Primary Prevention in Severe Coronary Calcium Score on Computed Tomography
ClinicalTrials.gov study NCT06676280. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Comparison of Cardiovascular Risk Stratification in Young People With Type 1 Diabetes by Coronary Calcium Score to ESC/ESA2019 Recommendations
ClinicalTrials.gov study NCT05283538. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multi Detector-Row Computed Tomography (MDCT) Calcium Score of Heart Transplanted Patients
ClinicalTrials.gov study NCT00294853. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Correlating the Measure of Retinal Vascular Density Through Angio-OCT with Calcium Score
ClinicalTrials.gov study NCT05624255. IPD Sharing: NO. Countries: 1. Publications: 0.
Association of Preoperative Coronary Calcium Score and Postoperative Atrial Arrythmia in Patients Undergoing Robot-assisted Esophagectomy: Prospective Observational Study
ClinicalTrials.gov study NCT07050030. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Pelvic CT with Agatston Calcium Score for Peyronie Disease
ClinicalTrials.gov study NCT05480683. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Coronary Calcium Scoring Versus Standard Care for Emergency Department Chest Pain Patients
ClinicalTrials.gov study NCT02828761. IPD Sharing: NO. Countries: 1. Publications: 0.
Study of Coronary Calcium Score as a Marker of Post-radiation Vascular Dysplasia in Adults Treated During Childhood for Cancer With Mediastinal Irradiation
ClinicalTrials.gov study NCT03604627. IPD Sharing: NO. Countries: 1. Publications: 0.
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