Journal
COMPUTER VISION AND IMAGE UNDERSTANDING
Volume 117, Issue 9, Pages 1119-1127Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.cviu.2012.12.006
Keywords
Inter-subject; Regularization; Abdomen; Shape model; Registration
Funding
- project Care4Me (Cooperative advanced research for medical efficiency)
- project Mediate (Patient friendly medical intervention)
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Deformable registration is prone to errors when it involves large and complex deformations, since the procedure can easily end up in a local minimum. To reduce the number of local minima, and thus the risk of misalignment, regularization terms based on prior knowledge can be incorporated in registration. We propose a regularization term that is based on statistical knowledge of the deformations that are to be expected. A statistical model, trained on the shapes of a set of segmentations, is integrated as a penalty term in a free-form registration framework. For the evaluation of our approach, we perform inter-patient registration of MR images, which were acquired for planning of radiation therapy of cervical cancer. The manual delineations of structures such as the bladder and the clinical target volume are available. For both structures, leave-one-patient-out registration experiments were performed. The propagated atlas segmentations were compared to the manual target segmentations by Dice similarity and Hausdorff distance. Compared with registration without the use of statistical knowledge, the segmentations were significantly improved, by 0.1 in Dice similarity and by 8 mm Hausdorff distance on average for both structures. (C) 2013 Elsevier Inc. All rights reserved.
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