title: Training models of anatomic shape variability.
abstract: 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.
authors.affiliation: Medical Image Display & Analysis Group, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. derek@cs.unc.edu
authors.forename: Derek
authors.initials: D
authors.lastname: Merck
authors.forename: Gregg
authors.initials: G
authors.lastname: Tracton
authors.forename: Rohit
authors.initials: R
authors.lastname: Saboo
authors.forename: Joshua
authors.initials: J
authors.lastname: Levy
authors.forename: Edward
authors.initials: E
authors.lastname: Chaney
authors.forename: Stephen
authors.initials: S
authors.lastname: Pizer
authors.forename: Sarang
authors.initials: S
authors.lastname: Joshi
mesh_terms: 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
references.citation: Neuroimage. 2004;23 Suppl 1:S151-60
references.pmid: 15501084
references.citation: IEEE Trans Med Imaging. 2004 Jul;23(7):903-21
references.pmid: 15250643
references.citation: Sci Am. 1999 Jun;280(6):62-9
references.pmid: 10349732
references.citation: Phys Med Biol. 2004 Jul 21;49(14):3067-87
references.pmid: 15357182
references.citation: Inf Process Med Imaging. 2007;20:333-45
references.pmid: 17633711
references.citation: IEEE Trans Med Imaging. 1999 Oct;18(10):840-50
references.pmid: 10628944
references.citation: Int J Radiat Oncol Biol Phys. 2005 Feb 1;61(2):561-9
references.pmid: 15667979
references.citation: Med Phys. 2005 May;32(5):1335-45
references.pmid: 15984685
references.citation: Inf Process Med Imaging. 2007;20:751-62
references.pmid: 17633745
references.citation: IEEE Trans Med Imaging. 2004 Aug;23(8):995-1005
references.pmid: 15338733
references.citation: Int J Radiat Oncol Biol Phys. 2000 Aug 1;48(1):289-302
references.pmid: 10925000
references.citation: Int J Radiat Oncol Biol Phys. 1999 Jun 1;44(3):665-75
references.pmid: 10348298
references.citation: Int J Radiat Oncol Biol Phys. 2004 Nov 1;60(3):973-80
references.pmid: 15465216
references.citation: Phys Med Biol. 1994 Mar;39(3):609-18
references.pmid: 15551602
references.citation: Int J Radiat Oncol Biol Phys. 2005 Mar 15;61(4):1258-66
references.pmid: 15752908
references.citation: IEEE Trans Med Imaging. 1999 Oct;18(10):828-39
references.pmid: 10628943
references.citation: Int J Radiat Oncol Biol Phys. 2005 Mar 1;61(3):954-60
references.pmid: 15708280
references.citation: Inf Process Med Imaging. 2005;19:701-12
references.pmid: 17354737
references.citation: IEEE Trans Med Imaging. 1999 Oct;18(10):1003-15
references.pmid: 10628959
references.citation: Med Image Anal. 1998 Sep;2(3):243-60
references.pmid: 9873902
references.citation: IEEE Trans Med Imaging. 2004 Aug;23(8):1006-20
references.pmid: 15338734
references.citation: IEEE Trans Med Imaging. 1999 Oct;18(10):825-7
references.pmid: 10660358
references.citation: Med Image Comput Comput Assist Interv. 2007;10(Pt 2):195-202
references.pmid: 18044569
references.citation: Med Image Anal. 2005 Apr;9(2):163-75
references.pmid: 15721231
references.citation: Med Phys. 2003 Mar;30(3):290-5
references.pmid: 12674227
references.citation: IEEE Trans Med Imaging. 2005 Mar;24(3):281-92
references.pmid: 15754979
references.citation: Med Phys. 2002 Jul;29(7):1403-5
references.pmid: 12148719
references.citation: Int J Radiat Oncol Biol Phys. 2005 Mar 1;61(3):725-35
references.pmid: 15708250
references.citation: J Comput Assist Tomogr. 1993 Mar-Apr;17(2):225-36
references.pmid: 8454749
references.citation: Phys Med Biol. 2005 Dec 21;50(24):5893-908
references.pmid: 16333162
references.citation: Phys Med Biol. 2005 Dec 21;50(24):5869-92
references.pmid: 16333161