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A quality score for coronary artery tree extraction results

Coronary artery trees (CATs) are often extracted to aid the fully automatic analysis of coronary artery disease on coronary computed tomography angiography (CCTA) images. Automatically extracted CATs often miss some arteries or include wrong extractions which require manual corrections before performing successive steps. For analyzing a large number of datasets, a manual quality check of the extraction results is time-consuming. This paper presents a method to automatically calculate quality scores for extracted CATs in terms of clinical significance of the extracted arteries and the completeness of the extracted CAT. Both right dominant (RD) and left dominant (LD) anatomical statistical models are generated and exploited in developing the quality score. To automatically determine which model should be used, a dominance type detection method is also designed. Experiments are performed on the automatically extracted and manually refined CATs from 42 datasets to evaluate the proposed quality score. In 39 (92.9%) cases, the proposed method is able to measure the quality of the manually refined CATs with higher scores than the automatically extracted CATs. In a 100-point scale system, the average scores for automatically and manually refined CATs are 82.0 (±15.8) and 88.9 (±5.4) respectively. The proposed quality score will assist the automatic processing of the CAT extractions for large cohorts which contain both RD and LD cases. To the best of our knowledge, this is the first time that a general quality score for an extracted CAT is presented.