TY - GEN
T1 - Structure specific atlas generation and its application to pancreas segmentation from contrasted abdominal CT volumes
AU - Karasawa, Ken’Ichi
AU - Kitasaka, Takayuki
AU - Oda, Masahiro
AU - Nimura, Yukitaka
AU - Hayashi, Yuichiro
AU - Fujiwara, Michitaka
AU - Misawa, Kazunari
AU - Rueckert, Daniel
AU - Mori, Kensaku
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Patient-specific atlas is a key technology for the recognition of the human anatomy from 3D medical images. Automated recognition of the pancreas is one main issue for computer-assisted diagnosis and therapy systems in the abdomen because many diseases of the pancreas are not accompanied by noticeable symptoms. In patient-specific atlas generation, hierarchical and mosaicing methods have been proposed to cope with individual differences in the position, orientation, and shape of the pancreas. Even though segmentation accuracy was improved by these methods, it remains lower than for other abdominal organs, such as the liver and the kidneys. The location of the pancreas strongly correlates with the location of vasculature systems, especially the splenic vein. In this paper, we propose a new structure specific atlas generation method that considers the structural information in atlas generation. As for the structural information, we enhance the vasculature using a vesselness filter. Similar volumes in a training dataset with respect to the vasculature structure are selected and used for atlas generation. Using 150 cases of contrast-enhanced 3D abdominal CT volumes, our experiment improved the mis-segmentation of the surrounding organs or such soft tissues as the duodenum.
AB - Patient-specific atlas is a key technology for the recognition of the human anatomy from 3D medical images. Automated recognition of the pancreas is one main issue for computer-assisted diagnosis and therapy systems in the abdomen because many diseases of the pancreas are not accompanied by noticeable symptoms. In patient-specific atlas generation, hierarchical and mosaicing methods have been proposed to cope with individual differences in the position, orientation, and shape of the pancreas. Even though segmentation accuracy was improved by these methods, it remains lower than for other abdominal organs, such as the liver and the kidneys. The location of the pancreas strongly correlates with the location of vasculature systems, especially the splenic vein. In this paper, we propose a new structure specific atlas generation method that considers the structural information in atlas generation. As for the structural information, we enhance the vasculature using a vesselness filter. Similar volumes in a training dataset with respect to the vasculature structure are selected and used for atlas generation. Using 150 cases of contrast-enhanced 3D abdominal CT volumes, our experiment improved the mis-segmentation of the surrounding organs or such soft tissues as the duodenum.
UR - http://www.scopus.com/inward/record.url?scp=84981328349&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-42016-5_5
DO - 10.1007/978-3-319-42016-5_5
M3 - Conference contribution
AN - SCOPUS:84981328349
SN - 9783319420158
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 47
EP - 56
BT - Medical Computer Vision
A2 - Kelm, Michael
A2 - Müller, Henning
A2 - Menze, Bjoern
A2 - Zhang, Shaoting
A2 - Metaxas, Dimitris
A2 - Langs, Georg
A2 - Montillo, Albert
A2 - Cai, Weidong
PB - Springer Verlag
T2 - International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
Y2 - 9 October 2015 through 9 October 2015
ER -
