Applying the Algorithm ‘Assessing Quality Using Image Registration Circuits’ (AQUIRC) to Multi-Atlas Segmentation.
Datteri, R.D., Asman, A.J., Landman, B.A., Dawant, B.M., “Applying the Algorithm ‘Assessing Quality Using Image Registration Circuits’ (AQUIRC) to Multi-Atlas Segmentation.” Proc. SPIE, Medical Imaging 2014: Image Processing, February 2014. †
Full Text: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1852464
Abstract
Multi-atlas registration-based segmentation is a popular technique in the medical imaging community, used to transform anatomical and functional information from a set of atlases onto a new patient that lacks this information. The accuracy of the projected information on the target image is dependent on the quality of the registrations between the atlas images and the target image. Recently, we have developed a technique called AQUIRC that aims at estimating the error of a non-rigid registration at the local level and was shown to correlate to error in a simulated case. Herein, we extend upon this work by applying AQUIRC to atlas selection at the local level across multiple structures in cases in which non-rigid registration is difficult. AQUIRC is applied to 6 structures, the brainstem, optic chiasm, left and right optic nerves, and the left and right eyes. We compare the results of AQUIRC to that of popular techniques, including Majority Vote, STAPLE, Non-Local STAPLE, and Locally-Weighted Vote. We show that AQUIRC can be used as a method to combine multiple segmentations and increase the accuracy of the projected information on a target image, and is comparable to cutting edge methods in the multi-atlas segmentation field. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).