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Training models of anatomic shape variability
Author(s) -
Merck Derek,
Tracton Gregg,
Saboo Rohit,
Levy Joshua,
Chaney Edward,
Pizer Stephen,
Joshi Sarang
Publication year - 2008
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2940188
Subject(s) - artificial intelligence , computer science , active shape model , medical imaging , shape analysis (program analysis) , computer vision , segmentation , point distribution model , population , image registration , image segmentation , set (abstract data type) , ground truth , active appearance model , probability distribution , statistical model , pattern recognition (psychology) , image (mathematics) , mathematics , statistics , static analysis , demography , sociology , programming language
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.