Automatic localization of landmark sets in head CT images with regression forests for image registration initialization

Dongqing Zhang; Yuan Liu; Jack H. Noble; Benoit M. Dawant

doi:10.1117/12.2216925

21 March 2016 Automatic localization of landmark sets in head CT images with regression forests for image registration initialization

Dongqing Zhang, Yuan Liu, Jack H. Noble, Benoit M. Dawant

Author Affiliations +

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97841M (2016) https://doi.org/10.1117/12.2216925
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Cochlear Implants (CIs) are electrode arrays that are surgically inserted into the cochlea. Individual contacts stimulate frequency-mapped nerve endings thus replacing the natural electro-mechanical transduction mechanism. CIs are programmed post-operatively by audiologists but this is currently done using behavioral tests without imaging information that permits relating electrode position to inner ear anatomy. We have recently developed a series of image processing steps that permit the segmentation of the inner ear anatomy and the localization of individual contacts. We have proposed a new programming strategy that uses this information and we have shown in a study with 68 participants that 78% of long term recipients preferred the programming parameters determined with this new strategy. A limiting factor to the large scale evaluation and deployment of our technique is the amount of user interaction still required in some of the steps used in our sequence of image processing algorithms. One such step is the rough registration of an atlas to target volumes prior to the use of automated intensity-based algorithms when the target volumes have very different fields of view and orientations. In this paper we propose a solution to this problem. It relies on a random forest-based approach to automatically localize a series of landmarks. Our results obtained from 83 images with 132 registration tasks show that automatic initialization of an intensity-based algorithm proves to be a reliable technique to replace the manual step.

Citation Download Citation

Dongqing Zhang, Yuan Liu, Jack H. Noble, and Benoit M. Dawant "Automatic localization of landmark sets in head CT images with regression forests for image registration initialization", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841M (21 March 2016); https://doi.org/10.1117/12.2216925

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