Abstract:
:We propose bilinear models for capturing and effectively decoupling the expected shape variations of an organ both across the patient population and within a specific patient. Bilinear models have been successfully introduced in other areas of computer vision, but they have rarely been used in medical imaging applications. Our particular interest is in modeling the shape variation of the prostate for potential use in radiation therapy treatment planning. Using a dataset of 204 prostate shapes contoured from CT imagery of 12 different patients, we build bilinear models and show that they can fit both training and testing shapes accurately. We also show how the bilinear model can adapt to a new patient using only a few example shapes, producing a patient-specific model that also reflects expected content variation learnt from a broader population. Finally, we evaluate the training and testing projection error, adaptation performance and image segmentation accuracy of the bilinear model compared to linear principal component analysis and hierarchical point distribution models with the same number of parameters.
journal_name
Phys Med Bioljournal_title
Physics in medicine and biologyauthors
Jeong Y,Radke RJ,Lovelock DMdoi
10.1088/0031-9155/55/13/010subject
Has Abstractpub_date
2010-07-07 00:00:00pages
3725-39issue
13eissn
0031-9155issn
1361-6560pii
S0031-9155(10)38540-Xjournal_volume
55pub_type
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