For radiation therapy of cervical cancer, segmentation of the cervix and the surrounding organs are needed. The aim is to develop a fully automatic method for the segmentation of all relevant organs. Our approach is an atlas-based segmentation, with a registration scheme that is aided by statistical knowledge of the deformations that are to be expected. A statistical model that acts on the boundary of an organ is included as a soft constraint in a free-form registration framework. As a first evaluation of our approach, we apply it to the segmentation of the bladder. Statistical models for the bladder were trained on a set of manual delineations. Experiments on a leave-one-patient-out basis were performed, with the quality defined as the Dice similarity to the manual segmentations. Compared to a registration without the use of statistical knowledge, the segmentations are slightly, but significantly improved. © 2012 Springer-Verlag.
CITATION STYLE
Berendsen, F. F., Van Der Heide, U. A., Langerak, T. R., Kotte, A. N. T. J., & Pluim, J. P. W. (2012). Segmentation of cervical images by inter-subject registration with a statistical organ model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7029 LNCS, pp. 240–247). https://doi.org/10.1007/978-3-642-28557-8_30
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