TY - GEN
T1 - Brain volume mapping for constructing volumetric statistical shape model
AU - Miyauchi, Shoko
AU - Morooka, Ken'ichi
AU - Miyagi, Yasushi
AU - Fukuda, Takaichi
AU - Kurazume, Ryo
N1 - Funding Information:
This work was supported by JST CREST Grant Number JPMJCR1786 including AIP challenge program and Grant-in-Aid for Research Activity Start-up Grant Number 18H06480, Japan.
Publisher Copyright:
© 2019 SPIE.
PY - 2019
Y1 - 2019
N2 - We propose a new method for mapping onto a brain volume model including inner organs with complicated shapes such as lateral ventricles. The proposed method is based on a volumetric Self-organizing Deformable Model (vSDM) which allows to control the mapping positions of inner organs while preserving geometrical features before and after the mapping. The control sometimes causes the self-intersection of the volume model. The solution for the self-intersection in vSDM is to move vertices of the volume model. However, when the inner organ has complicated shape, the vertex movement cannot always correct the self-intersection. To solve this problem, we extend vSDM by introducing a new process of editing the mesh structure of the volume model. Moreover, by applying the proposed method to six brain volume models, a volumetric Statistical Shape Model (SSM) is constructed which represents the shape variations of not only brain surface but also brain inner organs. From experimental results, we confirmed the volumetric SSM has an acceptable performance compared with general surface SSMs generated by organ surface models.
AB - We propose a new method for mapping onto a brain volume model including inner organs with complicated shapes such as lateral ventricles. The proposed method is based on a volumetric Self-organizing Deformable Model (vSDM) which allows to control the mapping positions of inner organs while preserving geometrical features before and after the mapping. The control sometimes causes the self-intersection of the volume model. The solution for the self-intersection in vSDM is to move vertices of the volume model. However, when the inner organ has complicated shape, the vertex movement cannot always correct the self-intersection. To solve this problem, we extend vSDM by introducing a new process of editing the mesh structure of the volume model. Moreover, by applying the proposed method to six brain volume models, a volumetric Statistical Shape Model (SSM) is constructed which represents the shape variations of not only brain surface but also brain inner organs. From experimental results, we confirmed the volumetric SSM has an acceptable performance compared with general surface SSMs generated by organ surface models.
KW - Model correspondence
KW - Volume Statistical Shape Model
KW - volumetric Self-organizing Deformable Model
UR - http://www.scopus.com/inward/record.url?scp=85063891595&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063891595&partnerID=8YFLogxK
U2 - 10.1117/12.2519819
DO - 10.1117/12.2519819
M3 - Conference contribution
AN - SCOPUS:85063891595
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - International Forum on Medical Imaging in Asia 2019
A2 - Kim, Jong Hyo
A2 - Lin, Feng
A2 - Fujita, Hiroshi
PB - SPIE
T2 - International Forum on Medical Imaging in Asia 2019
Y2 - 7 January 2019 through 9 January 2019
ER -