A method of 2D/3D registration of a statistical mouse atlas with a planar X-ray projection and an optical photo

被引:12
作者
Wang, Hongkai [1 ]
Stout, David B. [1 ]
Chatziioannou, Arion F. [1 ]
机构
[1] Univ Calif Los Angeles, Crump Inst Mol Imaging, Dept Mol & Med Pharmacol, Los Angeles, CA 90095 USA
关键词
Image registration; Mouse atlas; 2D/3D registration; Preclinical imaging; DYNAMIC PET IMAGES; 3D/2D REGISTRATION; SEGMENTATION; CT; RECONSTRUCTION; SYSTEM;
D O I
10.1016/j.media.2013.02.009
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The development of sophisticated and high throughput whole body small animal imaging technologies has created a need for improved image analysis and increased automation. The registration of a digital mouse atlas to individual images is a prerequisite for automated organ segmentation and uptake quantification. This paper presents a fully-automatic method for registering a statistical mouse atlas with individual subjects based on an anterior posterior X-ray projection and a lateral optical photo of the mouse silhouette. The mouse atlas was trained as a statistical shape model based on 83 organ-segmented microCT images. For registration, a hierarchical approach is applied which first registers high contrast organs, and then estimates low contrast organs based on the registered high contrast organs. To register the high contrast organs, a 2D-registration-back-projection strategy is used that deforms the 3D atlas based on the 2D registrations of the atlas projections. For validation, this method was evaluated using 55 subjects of preclinical mouse studies. The results showed that this method can compensate for moderate variations of animal postures and organ anatomy. Two different metrics, the Dice coefficient and the average surface distance, were used to assess the registration accuracy of major organs. The Dice coefficients vary from 0.31 +/- 0.16 for the spleen to 0.88 +/- 0.03 for the whole body, and the average surface distance varies from 0.54 +/- 0.06 mm for the lungs to 0.85 +/- 0.10 mm for the skin. The method was compared with a direct 3D deformation optimization (without 2D-registration-back-projection) and a single-subject atlas registration (instead of using the statistical atlas). The comparison revealed that the 2D-registration-back-projection strategy significantly improved the registration accuracy, and the use of the statistical mouse atlas led to more plausible organ shapes than the single-subject atlas. This method was also tested with shoulder xenograft tumor-bearing mice, and the results showed that the registration accuracy of most organs was not significantly affected by the presence of shoulder tumors, except for the lungs and the spleen. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:401 / 416
页数:16
相关论文
共 57 条
[1]   The space of human body shapes: reconstruction and parameterization from range scans [J].
Allen, B ;
Curless, B ;
Popovic, Z .
ACM TRANSACTIONS ON GRAPHICS, 2003, 22 (03) :587-594
[2]  
Baiker M., 2009, SPIE MED IMAGING 200
[3]  
Baiker M, 2011, LECT NOTES COMPUT SC, V6892, P516, DOI 10.1007/978-3-642-23629-7_63
[4]   Atlas-based whole-body segmentation of mice from low-contrast Micro-CT data [J].
Baiker, Martin ;
Milles, Julien ;
Dijkstra, Jouke ;
Henning, Tobias D. ;
Weber, Axel W. ;
Que, Ivo ;
Kaijzel, Eric L. ;
Lowik, Clemens W. G. M. ;
Reiber, Johan H. C. ;
Lelieveldt, Boudewijn P. F. .
MEDICAL IMAGE ANALYSIS, 2010, 14 (06) :723-737
[5]   2D-3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models [J].
Baka, N. ;
Kaptein, B. L. ;
de Bruijne, M. ;
van Walsum, T. ;
Giphart, J. E. ;
Niessen, W. J. ;
Lelieveldt, B. P. F. .
MEDICAL IMAGE ANALYSIS, 2011, 15 (06) :840-850
[6]   3D/2D registration and segmentation of scoliotic vertebrae using statistical models [J].
Benameur, S ;
Mignotte, M ;
Parent, S ;
Labelle, H ;
Skalli, W ;
de Guise, J .
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2003, 27 (05) :321-337
[7]   Landmark methods for forms without landmarks: Localizing group differences in outline shape [J].
Bookstein, FL .
PROCEEDINGS OF THE IEEE WORKSHOP ON MATHEMATICAL METHODS IN BIOMEDICAL IMAGE ANALYSIS, 1996, :279-289
[8]  
Bowden R., 1999, LEARNING NONLINEAR M
[9]   Hierarchical Statistical Shape Models of Multiobject Anatomical Structures: Application to Brain MRI [J].
Cerrolaza, Juan J. ;
Villanueva, Arantxa ;
Cabeza, Rafael .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2012, 31 (03) :713-724
[10]   Segmentation of mouse dynamic PET images using a multiphase level set method [J].
Cheng-Liao, Jinxiu ;
Qi, Jinyi .
PHYSICS IN MEDICINE AND BIOLOGY, 2010, 55 (21) :6549-6569