Computed tomography coronary angiogram images, annotations and associated data of normal and diseased arteries

• URL: http://arxiv.org/abs/2211.01859v1
• Date: Thu, 3 Nov 2022 14:50:43 GMT
• Title: Computed tomography coronary angiogram images, annotations and associated data of normal and diseased arteries
• Authors: Ramtin Gharleghi, Dona Adikari, Katy Ellenberger, Mark Webster, Chris Ellis, Arcot Sowmya, Sze-Yuan Ooi, Susann Beier
• Abstract summary: Computed Tomography Coronary Angiography (CTCA) is a non-invasive method to evaluate coronary artery anatomy and disease. To our knowledge there is no public dataset that includes centrelines and segmentation of the full coronary tree. Data can be used for a variety of research purposes, such as 3D printing patient-specific models, development and validation of segmentation algorithms, education and training of medical personnel and in-silico analyses such as testing of medical devices.
• Score: 8.516530964229814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computed Tomography Coronary Angiography (CTCA) is a non-invasive method to evaluate coronary artery anatomy and disease. CTCA is ideal for geometry reconstruction to create virtual models of coronary arteries. To our knowledge there is no public dataset that includes centrelines and segmentation of the full coronary tree. We provide anonymized CTCA images, voxel-wise annotations and associated data in the form of centrelines, calcification scores and meshes of the coronary lumen in 20 normal and 20 diseased cases. Images were obtained along with patient information with informed, written consent as part of Coronary Atlas (https://www.coronaryatlas.org/). Cases were classified as normal (zero calcium score with no signs of stenosis) or diseased (confirmed coronary artery disease). Manual voxel-wise segmentations by three experts were combined using majority voting to generate the final annotations. Provided data can be used for a variety of research purposes, such as 3D printing patient-specific models, development and validation of segmentation algorithms, education and training of medical personnel and in-silico analyses such as testing of medical devices.

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