Automatic Coronary Arteries Stenoses Detection in 3D CT Angiography
Cardiovascular lesions are the world's leading cause of mortality. Early detection of these diseases using less invasive techniques provides better therapeutic outcome, as well as reduces costs and risks, compared to an interventionist approach. In this work we propose a two-step severe cardiac stenoses detection approach. First we used the geometric model of the stenosis to detect suspicious areas and then we applied a false positives (FPs) removal step, based on lesions appearance properties. The algorithm was tested, in the context of the MICCAI challenge, on a 42 3D cardiac CT angiography database provided by the organizers. For the detection, a sensitivity of 47% and a PPV of 14% is obtained as compared to QCA, while a sensitivity of 35% and a PPV of 8% is achieved as compared to CTA.
Detection confusion tables
|Calc. cat.||QCA (per segment)||CTA (per lesion)|
The results of this method are based on the following centerlines: Rcadia team auto.
Detection (QCA per segment / CTA per lesion)
| ||%||rank||%||rank||%||rank||%||rank|| |
|0 (0 - 0.1)||20.0||11.0||6.7||14.0||14.3||14.0||3.7||14.0||13.2|
|1 (0.1 - 10)||100.0||1.0||7.7||11.0||80.0||4.0||13.3||14.0||7.5|
|2 (11 - 100)||42.9||11.0||9.4||17.0||36.4||12.0||5.6||16.0||14.0|
|3 (101 - 400)||60.0||8.0||15.8||14.0||47.4||9.0||12.0||15.0||11.5|
These results are based on 30 datasets and 19 submissions.