TY - GEN
T1 - Segmenting images by combining selected atlases on manifold
AU - Cao, Yihui
AU - Yuan, Yuan
AU - Li, Xuelong
AU - Turkbey, Baris
AU - Choyke, Peter L.
AU - Yan, Pingkun
PY - 2011
Y1 - 2011
N2 - Atlas selection and combination are two critical factors affecting the performance of atlas-based segmentation methods. In the existing works, those tasks are completed in the original image space. However, the intrinsic similarity between the images may not be accurately reflected by the Euclidean distance in this high-dimensional space. Thus, the selected atlases may be away from the input image and the generated template by combining those atlases for segmentation can be misleading. In this paper, we propose to select and combine atlases by projecting the images onto a low-dimensional manifold. With this approach, atlases can be selected according to their intrinsic similarity to the patient image. A novel method is also proposed to compute the weights for more efficiently combining the selected atlases to achieve better segmentation performance. The experimental results demonstrated that our proposed method is robust and accurate, especially when the number of training samples becomes large.
AB - Atlas selection and combination are two critical factors affecting the performance of atlas-based segmentation methods. In the existing works, those tasks are completed in the original image space. However, the intrinsic similarity between the images may not be accurately reflected by the Euclidean distance in this high-dimensional space. Thus, the selected atlases may be away from the input image and the generated template by combining those atlases for segmentation can be misleading. In this paper, we propose to select and combine atlases by projecting the images onto a low-dimensional manifold. With this approach, atlases can be selected according to their intrinsic similarity to the patient image. A novel method is also proposed to compute the weights for more efficiently combining the selected atlases to achieve better segmentation performance. The experimental results demonstrated that our proposed method is robust and accurate, especially when the number of training samples becomes large.
UR - http://www.scopus.com/inward/record.url?scp=82255183319&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23626-6_34
DO - 10.1007/978-3-642-23626-6_34
M3 - 会议稿件
C2 - 22003709
AN - SCOPUS:82255183319
SN - 9783642236259
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 272
EP - 279
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings
T2 - 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
Y2 - 18 September 2011 through 22 September 2011
ER -