| Autor: |
Qing Chu, Haobin Jiang, Libo Zhang, Dekun Zhu, Qianqian Yin, Hao Zhang, Bin Zhou, Wenzhang Zhou, Zhang Yue, Hong Lian, Lihui Liu, Yu Nie, Shengshou Hu |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Advanced Science, Vol 7, Iss 8, Pp n/a-n/a (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2198-3844 |
| DOI: |
10.1002/advs.201903592 |
| Popis: |
Abstract Congenital heart disease (CHD) is the major cause of morbidity/mortality in infancy and childhood. Using a mouse model to uncover the mechanism of CHD is essential to understand its pathogenesis. However, conventional 2D phenotyping methods cannot comprehensively exhibit and accurately distinguish various 3D cardiac malformations for the complicated structure of heart cavity. Here, a new automated tool based on microcomputed tomography (micro‐CT) image data sets known as computer‐assisted cardiac cavity tracking (CACCT) is presented, which can detect the connections between cardiac cavities and identify complicated cardiac malformations in mouse hearts automatically. With CACCT, researchers, even those without expert training or diagnostic experience of CHD, can identify complicated cardiac malformations in mice conveniently and precisely, including transposition of the great arteries, double‐outlet right ventricle and atypical ventricular septal defect, whose accuracy is equivalent to senior fetal cardiologists. CACCT provides an effective approach to accurately identify heterogeneous cardiac malformations, which will facilitate the mechanistic studies into CHD and heart development. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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