Method details

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Automatic detection of coronary artery stenosis in CTA based on vessel intensity and geometric features



Abstract:
In this paper, we present a fast and fully automatic learning based system that is capable of detecting coronary stenoses in Computed Tomography Angiography (CTA) caused by all types of plaques, e.g. soft, mixed, and calcified. We extract geometrical and intensity based features that can capture the characteristic properties of the coronary vessels.We evaluated our method on the Rotterdam Coronary Artery Stenoses Detection and Quantification Evaluation Framework on 42 datasets. On the 24 testing datasets, a sensitivity of 57% and a PPV of 18% is achieved as compared to QCA, while a sensitivity of 57% and a PPV of 32% is achieved as compared to CTA. This clearly indicates that our method is good at ruling out disease (low false negative detection value), but has limited performance to detect significant stenoses (> 50% luminal diameter reduction; high false positive rate).

Detection confusion tables

Calc. cat.QCA (per segment)CTA (per lesion)
 TPFPFNTP+FPTP+FNTPFPFNTP+FPTP+FN
All  15  63  13  78  28  25  71  22  96  47 
The results of this method are based on the following centerlines: LKEB team manual.

Detection (QCA per segment / CTA per lesion)

Calc. cat.QCA
Sens.
QCA
P.P.V.
CTA
Sens.
CTA
P.P.V.
Avg. rank
 %rank%rank%rank%rank 
0 (0 - 0.1)  0.0  15.0  0.0  15.0  28.6  11.0  25.0  9.0  12.5 
1 (0.1 - 10)  100.0  1.0  16.7  9.0  60.0  6.0  50.0  9.0  6.2 
2 (11 - 100)  57.1  8.0  25.0  8.0  63.6  6.0  36.8  5.0  6.8 
3 (101 - 400)  70.0  6.0  18.9  13.0  52.6  7.0  20.8  12.0  9.5 
4 (400+)  60.0  4.0  25.0  10.0  60.0  6.0  20.0  8.0  7.0 
All  53.6  10.0  19.2  11.0  53.2  6.0  26.0  9.0  9.0 
For ranking  53.6  10.0  19.2  11.0  53.2  6.0  26.0  9.0  9.0 
These results are based on 30 datasets and 19 submissions.