Coronary artery calcification assessment in National Lung Screening Trial CT images (DeepCAC2)
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911545019072512 |
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| author | Nürnberg, Leonard Bernatz, Simon Foldyna, Borek Lu, Michael T. Fedorov, Andrey Aerts, Hugo JWL |
| author_facet | Nürnberg, Leonard Bernatz, Simon Foldyna, Borek Lu, Michael T. Fedorov, Andrey Aerts, Hugo JWL |
| contents | Coronary artery calcification (CAC) is a strong predictor of cardiovascular risk but remains underutilized in clinical routine thoracic imaging due to the need for dedicated imaging protocols and manual annotation. We present DeepCAC2, a publicly available dataset containing automated CAC segmentations, coronary artery calcium scores, and derived risk categories generated from low-dose chest CT scans of the National Lung Screening Trial (NLST). Using a fully automated deep learning pipeline trained on expert-annotated cardiac CT data, we processed 127,776 CT scans from 26,228 individuals and generated standardized CAC segmentations and risk estimates for each acquisition. We already provide a public dashboard as a simple tool to visually inspect a random subset of 200 NLST patients of the dataset. The dataset will be released with DICOM-compatible segmentation objects and structured metadata to support reproducible downstream analysis. The deep learning pipeline will be made publicly available as a DICOM-compatible MHub.ai container. DeepCAC2 provides a transparent, large-scale, public, fully reproducible resource for research in cardiovascular risk assessment, opportunistic screening, and imaging biomarker development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24633 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Coronary artery calcification assessment in National Lung Screening Trial CT images (DeepCAC2) Nürnberg, Leonard Bernatz, Simon Foldyna, Borek Lu, Michael T. Fedorov, Andrey Aerts, Hugo JWL Image and Video Processing Coronary artery calcification (CAC) is a strong predictor of cardiovascular risk but remains underutilized in clinical routine thoracic imaging due to the need for dedicated imaging protocols and manual annotation. We present DeepCAC2, a publicly available dataset containing automated CAC segmentations, coronary artery calcium scores, and derived risk categories generated from low-dose chest CT scans of the National Lung Screening Trial (NLST). Using a fully automated deep learning pipeline trained on expert-annotated cardiac CT data, we processed 127,776 CT scans from 26,228 individuals and generated standardized CAC segmentations and risk estimates for each acquisition. We already provide a public dashboard as a simple tool to visually inspect a random subset of 200 NLST patients of the dataset. The dataset will be released with DICOM-compatible segmentation objects and structured metadata to support reproducible downstream analysis. The deep learning pipeline will be made publicly available as a DICOM-compatible MHub.ai container. DeepCAC2 provides a transparent, large-scale, public, fully reproducible resource for research in cardiovascular risk assessment, opportunistic screening, and imaging biomarker development. |
| title | Coronary artery calcification assessment in National Lung Screening Trial CT images (DeepCAC2) |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2603.24633 |