"Segmentation of the prostate in MR images by atlas matching"

Stefan Klein, Uulke A. van der Heide, Bas W. Raaymakers, Alexis N.T.J. Kotte, Marius Staring and Josien P.W. Pluim


Prostate cancer treatment by radiation therapy requires an accurate localisation of the prostate. For the treatment planning, primarily computed tomography (CT) images are used, but increasingly magnetic resonance (MR) images are added, because of their soft-tissue contrast. In current practice at our hospital, a manual delineation of the prostate is made, based on the CT and MR scans, which is a labour-intensive task. We propose an automatic segmentation method, based on nonrigid registration of a set of prelabelled MR atlas images. The algorithm consists of three stages. Firstly, the target image is nonrigidly registered with each atlas image, using mutual information as the similarity measure. After that, the best registered atlas images are selected by comparing the mutual information values after registration. Finally, the segmentation is obtained by averaging the selected deformed segmentations and thresholding the result. The method is evaluated on 22 images by calculating the overlap of automatic and manual segmentations. This results in a median Dice similarity coefficient of 0.82.



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Copyright © 2007 by the authors. Published version © 2007 by . Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.


BibTeX entry

author = "{Stefan Klein and Uulke A. van der Heide and Bas W. Raaymakers and Alexis N.T.J. Kotte and Marius Staring and Josien P.W. Pluim}",
title = "{Segmentation of the prostate in MR images by atlas matching}",
booktitle = "{IEEE International Symposium on Biomedical Imaging (ISBI)}",
editor = "{J.A. Fessler and T.S. Denney Jr.}",
address = "{Washington, USA}",
pages = "{1300 - 1303}",
month = "{April}",
year = "{2007}",

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