TY - GEN
T1 - Multiple-organ segmentation by graph cuts with supervoxel nodes
AU - Takaoka, Toshiya
AU - Mochizuki, Yoshihiko
AU - Ishikawa, Hiroshi
N1 - Funding Information:
This work was partially supported by JSPS KAKENHI Grant Number 26108003 as well as CREST from JST.
PY - 2017/7/19
Y1 - 2017/7/19
N2 - Improvement in medical imaging technologies has made it possible for doctors to directly look into patients' bodies in ever finer details. However, since only the cross-sectional image can be directly seen, it is essential to segment the volume into organs so that their shape can be seen as 3D graphics of the organ boundary surfaces. Segmentation is also important for quantitative measurement for diagnosis. Here, we introduce a novel higher-precision method to segment multiple organs using graph cuts within medical images such as CT-scanned images. We utilize super voxels instead of voxels as the units of segmentation, i.e., the nodes in the graphical model, and design the energy function to minimize accordingly. We utilize SLIC super voxel algorithm and verify the performance of our segmentation algorithm by energy minimization comparing to the ground truth.
AB - Improvement in medical imaging technologies has made it possible for doctors to directly look into patients' bodies in ever finer details. However, since only the cross-sectional image can be directly seen, it is essential to segment the volume into organs so that their shape can be seen as 3D graphics of the organ boundary surfaces. Segmentation is also important for quantitative measurement for diagnosis. Here, we introduce a novel higher-precision method to segment multiple organs using graph cuts within medical images such as CT-scanned images. We utilize super voxels instead of voxels as the units of segmentation, i.e., the nodes in the graphical model, and design the energy function to minimize accordingly. We utilize SLIC super voxel algorithm and verify the performance of our segmentation algorithm by energy minimization comparing to the ground truth.
UR - http://www.scopus.com/inward/record.url?scp=85027887026&partnerID=8YFLogxK
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U2 - 10.23919/MVA.2017.7986891
DO - 10.23919/MVA.2017.7986891
M3 - Conference contribution
AN - SCOPUS:85027887026
T3 - Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
SP - 424
EP - 427
BT - Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IAPR International Conference on Machine Vision Applications, MVA 2017
Y2 - 8 May 2017 through 12 May 2017
ER -