Real-time detection of patient head position and cephalometric landmarks from neuro-interventional procedure images using machine learning for patient eye-lens dose prediction

Jacob Collins; Jonathan Troville; Kyle A. Williams; Stephen Rudin; Daniel R. Bednarek

doi:10.1117/12.2611184

4 April 2022 Real-time detection of patient head position and cephalometric landmarks from neuro-interventional procedure images using machine learning for patient eye-lens dose prediction

Jacob Collins, Jonathan Troville, Kyle A. Williams, Stephen Rudin, Daniel R. Bednarek

Author Affiliations +

Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging; 120314A (2022) https://doi.org/10.1117/12.2611184
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Machine learning (ML) models were investigated to automatically detect the patient head shift from isocenter and cephalometric landmark locations as a surrogate for head size. Fluoroscopic images of a Kyoto Kagaku anthropomorphic head phantom were taken at various head shifts and magnification modes, to create an image database. One ML model predicts the patient head shift and the other model predicts the coordinates of the anatomical landmarks. The goal is to implement these two separate models into the Dose Tracking System (DTS) developed by our group for eye-lens dose prediction and eliminate the need for manual input by clinical staff.

Conference Presentation

Citation Download Citation

Jacob Collins, Jonathan Troville, Kyle A. Williams, Stephen Rudin, and Daniel R. Bednarek "Real-time detection of patient head position and cephalometric landmarks from neuro-interventional procedure images using machine learning for patient eye-lens dose prediction", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 120314A (4 April 2022); https://doi.org/10.1117/12.2611184

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