open access
Semi-automatic segmentation and surface reconstruction of computed tomography images by using rotoscoping and warping techniques
, 1, 1
Affiliations- 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
open access
Abstract
Background: 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.
Materials and methods: Therefore, in this study, we developed workflows for semi-automatic segmentation and surface reconstruction, using rotoscoping and warping techniques.
Results: 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.
Conclusions: 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.
Abstract
Background: 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.
Materials and methods: Therefore, in this study, we developed workflows for semi-automatic segmentation and surface reconstruction, using rotoscoping and warping techniques.
Results: 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.
Conclusions: 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.
Keywords
cross-sectional anatomy, segmentation, surface reconstruction, three-dimensional imaging, landmark, surface anatomy, rotoscoping
Supp./Additional Files (1)
About this articleTitle
Semi-automatic segmentation and surface reconstruction of computed tomography images by using rotoscoping and warping techniques
Journal
Issue
Article type
Original article
Pages
156-161
Published online
2019-04-11
Page views
2667
Article views/downloads
641
DOI
Pubmed
Bibliographic record
Folia Morphol 2020;79(1):156-161.
Keywords
cross-sectional anatomy
segmentation
surface reconstruction
three-dimensional imaging
landmark
surface anatomy
rotoscoping
Authors
S. K. Park
B. K. Kim
D. S. Shin
References (31)- Badshah M, Soames R, Ibrahim M, et al. Surface anatomy of major anatomical landmarks of the neck in an adult population: A Ct Evaluation of Vertebral Level. Clin Anat. 2017; 30(6): 781–787.
- Badshah M, Soames R, Khan MJ, et al. Revisiting thoracic surface anatomy in an adult population: A computed tomography evaluation of vertebral level. Clin Anat. 2017; 30(2): 227–236.
- Dong X, Lei Y, Wang T, et al. Automatic multiorgan segmentation in thorax CT images using U-net-GAN. Med Phys. 2019; 46(5): 2157–2168.
- Fischer NJ, Morreau J, Sugunesegran R, et al. A reappraisal of pediatric thoracic surface anatomy. Clin Anat. 2017; 30(6): 788–794.
- Gargiulo P, Helgason T, Ramon C, et al. CT and MRI Assessment and Characterization Using Segmentation and 3D Modeling Techniques: Applications to Muscle, Bone and Brain. Eur J Transl Myol. 2014; 24(1): 3298.
- Hu P, Wu Fa, Peng J, et al. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets. Int J Comput Assist Radiol Surg. 2017; 12(3): 399–411.
- Jain N, Bhargava A, Pareek V, et al. Does seed size and surface anatomy play role in combating phytotoxicity of nanoparticles? Ecotoxicology. 2017; 26(2): 238–249.
- Kamiya N, Zhou X, Chen H, et al. Automated segmentation of recuts abdominis muscle using shape model in X-ray CT images. Conf Proc IEEE Eng Med Biol Soc. 2011; 2011: 7993–7996.
- Kamiya N, Zhou X, Chen H, et al. Automated segmentation of psoas major muscle in X-ray CT images by use of a shape model: preliminary study. Radiol Phys Technol. 2012; 5(1): 5–14.
- Kianmehr N, Hasanzadeh A, Naderi F, et al. A randomized blinded comparative study of clinical response to surface anatomy guided injection versus sonography guided injection of hyaloronic acid in patients with primary knee osteoarthritis. Int J Rheum Dis. 2018; 21(1): 134–139.
- Kim BC, Chung MS, Kim HJ, et al. Sectioned images and surface models of a cadaver for understanding the deep circumflex iliac artery flap. J Craniofac Surg. 2014; 25(2): 626–629.
- Kim BC, Chung MS, Park HS, et al. Accessible and informative sectioned images and surface models of the maxillofacial area for orthognathic surgery. Folia Morphol. 2015; 74(3): 346–351.
- Kohlberger T, Sofka M, Zhang J, et al. Automatic multi-organ segmentation using learning-based segmentation and level set optimization. Med Image Comput Comput Assist Interv. 2011; 14(Pt 3): 338–345.
- Kwon K, Shin DS, Shin BS, et al. Virtual endoscopic and laparoscopic exploration of stomach wall based on a cadaver's sectioned images. J Korean Med Sci. 2015; 30(5): 658–661.
- Morris VB, Corrigan D, Sealy U, et al. Anatomy from the outside in: a new on-line surface anatomy guide. J Anat. 2016; 228(1): 24–25.
- Nadesan T, Keough N, Suleman FE, et al. Apprasial of the surface anatomy of the Thorax in an adolescent population. Clin Anat. 2019; 32(6): 762–769.
- Popuri K, Cobzas D, Esfandiari N, et al. Body composition assessment in axial CT images using fem-based automatic segmentation of skeletal muscle. IEEE Trans Med Imaging. 2016; 35(2): 512–520.
- Quiles C, Constantino JA, Gañán Y, et al. Stereophotogrammetric surface anatomy of the anterior cruciate ligament's tibial footprint: Precise osseous structure and distances to arthroscopically-relevant landmarks. Knee. 2018; 25(4): 531–544.
- Shen XH, Su BY, Liu JJ, et al. A reappraisal of adult thoracic and abdominal surface anatomy via CT scan in Chinese population. Clin Anat. 2016; 29(2): 165–174.
- Shen XH, Xue HD, Chen Yu, et al. A reassessment of cervical surface anatomy via CT scan in an adult population. Clin Anat. 2017; 30(3): 330–335.
- Shin DS, Chung MS, Park JS, et al. Portable document format file showing the surface models of cadaver whole body. J Korean Med Sci. 2012; 27(8): 849–856.
- Shin DS, Jang HG, Hwang SB, et al. Two-dimensional sectioned images and three-dimensional surface models for learning the anatomy of the female pelvis. Anat Sci Educ. 2013; 6(5): 316–323.
- Shin DS, Jang HG, Park JS, et al. Accessible and informative sectioned images and surface models of a cadaver head. J Craniofac Surg. 2012; 23(4): 1176–1180.
- Shin DS, Kim HJ, Kim BC. Sectioned images and surface models of a cadaver for understanding the free vascularised anterior rib flap. Folia Morphol. 2017; 76(1): 117–122.
- Shin DS, Lee S, Park HS, et al. Segmentation and surface reconstruction of a cadaver heart on Mimics software. Folia Morphol. 2015; 74(3): 372–377.
- Shin DS, Park SK. Surface Reconstruction and Optimization of Cerebral Cortex for Application Use. J Craniofac Surg. 2016; 27(2): 489–492.
- Shin DS, Shim YJ, Kim BC. Sectioned images and 3D models of a cadaver head with reference to dermal filler injection. Ann Anat. 2018; 217: 34–39.
- Shin DS, Shim YJ, Kim BC. Sectioned images and surface models of a cadaver head with reference to botulinum neurotoxin injection. Folia Morphol. 2018; 77(3): 564–569.
- Stern J. Surface anatomy of the trunk based on CT data. Clin Anat. 2016; 29(2): 130.
- Uzun C, Atman ED, Ustuner E, et al. Surface anatomy and anatomical planes in the adult turkish population. Clin Anat. 2016; 29(2): 183–190.
- Yokota F, Otake Y, Takao M, et al. Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method. Int J Comput Assist Radiol Surg. 2018; 13(7): 977–986.
