| Literature DB >> 29663370 |
Hongkai Wang1, Xiaobang Sun1,2, Tongning Wu3, Congsheng Li3, Zhonghua Chen1, Meiying Liao1, Mengci Li1, Wen Yan1, Hui Huang1, Jia Yang1, Ziyu Tan1, Libo Hui1, Yue Liu1, Hang Pan1, Yue Qu1, Zhaofeng Chen1, Liwen Tan4, Lijuan Yu5, Hongcheng Shi6, Li Huo7, Yanjun Zhang8, Xin Tang9, Shaoxiang Zhang4, Changjian Liu9.
Abstract
In recent years, there has been increasing demand for personalized anatomy modelling for medical and industrial applications, such as ergonomics device development, clinical radiological exposure simulation, biomechanics analysis, and 3D animation character design. In this study, we constructed deformable torso phantoms that can be deformed to match the personal anatomy of Chinese male and female adults. The phantoms were created based on a training set of 79 trunk computed tomography (CT) images (41 males and 38 females) from normal Chinese subjects. Major torso organs were segmented from the CT images, and the statistical shape model (SSM) approach was used to learn the inter-subject anatomical variations. To match the personal anatomy, the phantoms were registered to individual body surface scans or medical images using the active shape model method. The constructed SSM demonstrated anatomical variations in body height, fat quantity, respiratory status, organ geometry, male muscle size, and female breast size. The masses of the deformed phantom organs were consistent with Chinese population organ mass ranges. To validate the performance of personal anatomy modelling, the phantoms were registered to the body surface scan and CT images. The registration accuracy measured from 22 test CT images showed a median Dice coefficient over 0.85, a median volume recovery coefficient (RCvlm ) between 0.85 and 1.1, and a median averaged surface distance (ASD) < 1.5 mm. We hope these phantoms can serve as computational tools for personalized anatomy modelling for the research community.Entities:
Keywords: Chinese visible human; digital human phantom; personalized anatomy modelling; personalized biomechanics; personalized dosimetry; statistical shape model
Mesh:
Year: 2018 PMID: 29663370 PMCID: PMC5987821 DOI: 10.1111/joa.12815
Source DB: PubMed Journal: J Anat ISSN: 0021-8782 Impact factor: 2.610