Training models of anatomic shape variability.
Learning probability distributions of the shape of anatomic structures requires fitting shape representations to human expert segmentations from training sets of medical images. The quality of statistical segmentation and registration methods is directly related to the quality of this initial shape fitting, yet the subject is largely overlooked or described in an ad hoc way. This article presents a set of general principles to guide such training. Our novel method is to jointly estimate both the best geometric model for any given image and the shape distribution for the entire population of training images by iteratively relaxing purely geometric constraints in favor of the converging shape probabilities as the fitted objects converge to their target segmentations. The geometric constraints are carefully crafted both to obtain legal, nonself-interpenetrating shapes and to impose the model-to-model correspondences required for useful statistical analysis. The paper closes with example applications of the method to synthetic and real patient CT image sets, including same patient male pelvis and head and neck images, and cross patient kidney and brain images. Finally, we outline how this shape training serves as the basis for our approach to IGRT/ART.
Merck
Derek
D
Medical Image Display & Analysis Group, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. derek@cs.unc.edu
Tracton
Gregg
G
Saboo
Rohit
R
Levy
Joshua
J
Chaney
Edward
E
Pizer
Stephen
S
Joshi
Sarang
S
D001921:Brain / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D006257:Head / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007668:Kidney / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D008297:Male; D008953:Models, Anatomic; D009333:Neck / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D010363:Pattern Recognition, Automated / Q000379:methods*; D010388:Pelvis / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D011859:Radiography
Neuroimage. 2004;23 Suppl 1:S151-60
15501084
IEEE Trans Med Imaging. 2004 Jul;23(7):903-21
15250643
Sci Am. 1999 Jun;280(6):62-9
10349732
Phys Med Biol. 2004 Jul 21;49(14):3067-87
15357182
Inf Process Med Imaging. 2007;20:333-45
17633711
IEEE Trans Med Imaging. 1999 Oct;18(10):840-50
10628944
Int J Radiat Oncol Biol Phys. 2005 Feb 1;61(2):561-9
15667979
Med Phys. 2005 May;32(5):1335-45
15984685
Inf Process Med Imaging. 2007;20:751-62
17633745
IEEE Trans Med Imaging. 2004 Aug;23(8):995-1005
15338733
Int J Radiat Oncol Biol Phys. 2000 Aug 1;48(1):289-302
10925000
Int J Radiat Oncol Biol Phys. 1999 Jun 1;44(3):665-75
10348298
Int J Radiat Oncol Biol Phys. 2004 Nov 1;60(3):973-80
15465216
Phys Med Biol. 1994 Mar;39(3):609-18
15551602
Int J Radiat Oncol Biol Phys. 2005 Mar 15;61(4):1258-66
15752908
IEEE Trans Med Imaging. 1999 Oct;18(10):828-39
10628943
Int J Radiat Oncol Biol Phys. 2005 Mar 1;61(3):954-60
15708280
Inf Process Med Imaging. 2005;19:701-12
17354737
IEEE Trans Med Imaging. 1999 Oct;18(10):1003-15
10628959
Med Image Anal. 1998 Sep;2(3):243-60
9873902
IEEE Trans Med Imaging. 2004 Aug;23(8):1006-20
15338734
IEEE Trans Med Imaging. 1999 Oct;18(10):825-7
10660358
Med Image Comput Comput Assist Interv. 2007;10(Pt 2):195-202
18044569
Med Image Anal. 2005 Apr;9(2):163-75
15721231
Med Phys. 2003 Mar;30(3):290-5
12674227
IEEE Trans Med Imaging. 2005 Mar;24(3):281-92
15754979
Med Phys. 2002 Jul;29(7):1403-5
12148719
Int J Radiat Oncol Biol Phys. 2005 Mar 1;61(3):725-35
15708250
J Comput Assist Tomogr. 1993 Mar-Apr;17(2):225-36
8454749
Phys Med Biol. 2005 Dec 21;50(24):5893-908
16333162
Phys Med Biol. 2005 Dec 21;50(24):5869-92
16333161