Automated detection method of thoracic aorta calcification from non-contrast CT images using mediastinal anatomical label map

Progression of thoracic aortic calcification (TAC) has been shown to be associated with hard cardiovascular events including stroke and all-cause mortality as well as coronary events. In this study, we propose an automated detection method of TACs of non-contrast CT images using mediastinal anatomical label map. This method consists of two steps: (1) the construction of a mediastinal anatomical label map, and (2) the detection of TACs using the intensity and the mediastinal anatomical label map. The proposed method was applied to two non-contrast CT image datasets: 24 cases of chronic thromboembolic pulmonary hypertension (CTEPH) and 100 non-CTEPH cases of low-dose CT screening. The method was compared with two-dimensional U-Nets and the Swin UNETR. The results showed that the method achieved significantly higher F1 score of 0.937 than other methods for the non-CTEPH case dataset (p-value < 0.05, pairwise Wilcoxon signed rank test with Bonferroni correction).

Hidenobu Suzuki, Yoshiki Kawata, Toshihiko Sugiura, Nobuhiro Tanabe, Yuji Matsumoto, Takaaki Tsuchida, Masahiko Kusumoto, Kazuyoshi Marumo, Masahiro Kaneko, and Noboru Niki "Automated detection method of thoracic aorta calcification from non-contrast CT images using mediastinal anatomical label map", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 1246519 (7 April 2023); https://doi.org/10.1117/12.2654043

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