Automatic fiducial marker detection and localization in CT images: a combined approach

Milovan Regodic; Zoltan Bardosi; Wolfgang Freysinger

doi:10.1117/12.2548852

16 March 2020 Automatic fiducial marker detection and localization in CT images: a combined approach

Milovan Regodic, Zoltan Bardosi, Wolfgang Freysinger

Author Affiliations +

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 113151Y (2020) https://doi.org/10.1117/12.2548852
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Patient-to-image registration is a key step for guidance in computer-assisted surgery during interventions like cochlear implant or deep brain stimulation surgeries. Automatizing fiducial detection and localization in pre-operative images of the patient can lead to better registration accuracy, reduced human errors and shorter intervention time. We present an algorithm that builds on earlier approaches with morphological functions and pose estimation algorithms. A Convolutional Neural Network is proposed for the fiducial classification task. A digital experiment, with cone-beam CT imaging software, is performed to determine the accuracy of the algorithm. The inputs to this software are virtual phantoms represented as 3D surface models (meshes) of skull and (screw and spherical) fiducial markers combined with specific imaging parameters like material properties, detector resolution, etc. The software generates realistic CT images for establishing a ground-truth measure to validate the algorithm. The localized fiducial positions in the image by the presented algorithm were compared to the actual known positions in the phantom models. The difference represents the fiducial localization error (FLE). Validation data sets with different slice thicknesses contain screws and spherical markers of different dimensions. The achieved FLE mean and its standard deviation for a screw and spherical marker are 58 (14) μm and 14 (6) μm, respectively. Large marker volume and smaller voxel size yield smaller FLEs. Furthermore, we found that attenuating noise by mesh smoothing has a minor effect on localization accuracy.

Citation Download Citation

Milovan Regodic, Zoltan Bardosi, and Wolfgang Freysinger "Automatic fiducial marker detection and localization in CT images: a combined approach", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151Y (16 March 2020); https://doi.org/10.1117/12.2548852

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE