Bae, Jang Pyo Vania, Malinda Yoon, Siyeop Cheon, Sojeong Yoon, Chang Hwan Lee, Deukhee 2024-01-19T17:04:21Z 2024-01-19T17:04:21Z 2021-09-05 2020-07 2076-3417 https://pubs.kist.re.kr/handle/201004/118451 The creation of 3D models for cardiac mapping systems is time-consuming, and the models suffer from issues with repeatability among operators. The present study aimed to construct a double-shaped model composed of the left ventricle and left atrium. We developed cascaded-regression-based segmentation software with probabilistic point and appearance correspondence. Group-wise registration of point sets constructs the point correspondence from probabilistic matches, and the proposed method also calculates appearance correspondence from these probabilistic matches. Final point correspondence of group-wise registration constructed independently for three surfaces of the double-shaped model. Stochastic appearance selection of cascaded regression enables the effective construction in the aspect of memory usage and computation time. The two correspondence construction methods of active appearance models were compared in terms of the paired segmentation of the left atrium (LA) and left ventricle (LV). The proposed method segmented 35 cardiac CTs in six-fold cross-validation, and the symmetric surface distance (SSD), Hausdorff distance (HD), and Dice coefficient (DC), were used for evaluation. The proposed method produced 1.88 +/- 0.37 mm of LV SSD, 2.25 +/- 0.51 mm* of LA SSD, and 2.06 +/- 0.34 mm* of the left heart (LH) SSD. Additionally, DC was 80.45% +/- 4.27%***, where *p<0.05, **p<0.01, and ***p<0.001. All p values derive from pairedt-tests comparing iterative closest registration with the proposed method. In conclusion, the authors developed a cascaded regression framework for 3D cardiac CT segmentation. English MDPI ACTIVE APPEARANCE MODELS ANATOMICAL STRUCTURES SHAPE HEART REGISTRATION Cascaded Regression-Based Segmentation of Cardiac CT under Probabilistic Correspondences Article 10.3390/app10144947 1 APPLIED SCIENCES-BASEL, v.10, no.14 APPLIED SCIENCES-BASEL 10 14 scie scopus 000558200200001 2-s2.0-85088649572 Chemistry, Multidisciplinary Engineering, Multidisciplinary Materials Science, Multidisciplinary Physics, Applied Chemistry Engineering Materials Science Physics Article ACTIVE APPEARANCE MODELS ANATOMICAL STRUCTURES SHAPE HEART REGISTRATION cascaded regression group-wise correspondence construction cardiac CT heart segmentation