Automatic Coronary Calcium Scoring in Cardiac CT Angiography Using Convolutional Neural Networks

@inproceedings{Wolterink2015AutomaticCC,
  title={Automatic Coronary Calcium Scoring in Cardiac CT Angiography Using Convolutional Neural Networks},
  author={Jelmer M. Wolterink and Tim Leiner and Max A. Viergever and Ivana I{\vs}gum},
  booktitle={MICCAI},
  year={2015}
}
The amount of coronary artery calcification CAC is a strong and independent predictor of cardiovascular events. [] Key Method The study included CCTA scans of 50 patients equally distributed over five cardiovascular risk categories. CAC in CCTA was identified in two stages. In the first stage, potential CAC voxels were identified using a convolutional neural network CNN.

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References

SHOWING 1-10 OF 24 REFERENCES

Automatic Coronary Calcium Scoring in Non-Contrast-Enhanced ECG-Triggered Cardiac CT With Ambiguity Detection

A system that automatically quantifies total patient and per coronary artery CAC in non-contrast-enhanced, ECG-triggered cardiac CT and identifies candidate calcifications that cannot be automatically labeled with high certainty and optionally presents these to an expert for review.

Vessel specific coronary artery calcium scoring: an automatic system.

Quantifying coronary artery calcification from a contrast-enhanced cardiac computed tomography angiography study.

Quantification of CAC from a single contrast-enhanced CCTA scan is feasible and correlates well with Standard-CAC, and larger, multicentre studies are needed to validate the universal applicability of Cac quantified using CCTa.

Automatic detection and quantification of the Agatston coronary artery calcium score on contrast computed tomography angiography

Fully automatic detection of Agatston Cac score on contrast CTA is feasible and showed high correlation with non-contrast CT CAC score, which could imply a radiation dose reduction and time saving by omitting the non-Contrast scan.

Automatic detection and quantification of coronary calcium on 3D CT angiography data

A novel approach to the fully automatic segmentation and quantification of calcified lesions in coronary computed tomography angiograms that includes a robust threshold determination algorithm based on a histogram calculated from an automatically generated vessel tree is presented.

Fully automatic model-based calcium segmentation and scoring in coronary CT angiography

High diagnostic performance, combined with the benefits of the fully automatic solution, suggests that the proposed technique can be used to eliminate the need in a separate CS CT scan as part of the cCTA examination, thus reducing the radiation exposure and simplifying the procedure.

A method for coronary artery calcium scoring using contrast-enhanced computed tomography.

Deriving coronary artery calcium scores from CT coronary angiography: a proposed algorithm for evaluating stable chest pain

If incorporated into stable chest pain guidelines the need for further functional testing or invasive angiography could be determined from CTCA alone, supporting a change to the current guidelines.

Localizing Calcifications in Cardiac CT Data Sets Using a New Vessel Segmentation Approach

This work presents a method for localizing calcifications by employing a newly developed vessel segmentation approach and an approach for automatically detecting calcified regions that combines diameter information and gray value analysis.

Assessment of Agatston coronary artery calcium score using contrast-enhanced CT coronary angiography.

The AgatSTON calcium score derived from CTA images shows good correlation with unenhanced CT calcium score and is highly reproducible, however, higher Agatston scores are systematically underestimated when derived fromCTA images.