Park S K, Kim B K, Shin D S
Department of Emergency Medical Technology, Gachon University of College of Health Science, 119 Hambakmoe-ro, Yeonsu-gu, Incheon 406-799, South Korea, incheon, Korea, Republic Of.
Folia Morphol (Warsz). 2020;79(1):156-161. doi: 10.5603/FM.a2019.0045. Epub 2019 Apr 17.
Quick and large-scale segmentation along with three-dimensional (3D) reconstruction is necessary to make precise 3D musculoskeletal models for surface anatomy education, palpation training, medical communication, morphology research, and virtual surgery simulation. However, automatic segmentation of the skin and muscles remain undeveloped.
Therefore, in this study, we developed workflows for semi-automatic segmentation and surface reconstruction, using rotoscoping and warping techniques.
The techniques were applied to multi detector computed tomography images, which were optimised to quickly generate surface models of the skin and the anatomical structures underlying the fat tissue.
The workflows developed in this study are expected to enable researchers to create segmented images and optimised surface models from any set of serially sectioned images quickly and conveniently. Moreover, these optimised surface models can easily be modified for further application or educational use.
为了制作用于表面解剖学教育、触诊训练、医学交流、形态学研究和虚拟手术模拟的精确三维肌肉骨骼模型，快速大规模分割以及三维重建是必要的。然而，皮肤和肌肉的自动分割仍未得到充分发展。
因此，在本研究中，我们使用图像描摹和变形技术开发了半自动分割和表面重建的工作流程。
这些技术应用于多探测器计算机断层扫描图像，这些图像经过优化，可快速生成皮肤和脂肪组织下方解剖结构的表面模型。
本研究中开发的工作流程有望使研究人员能够从任何一组连续切片图像中快速方便地创建分割图像和优化的表面模型。此外，这些优化的表面模型可以很容易地修改以用于进一步的应用或教育用途。
