End-to-end forecasting of needle trajectory in percutaneous ablation

Mark A. Pinnock; Yipeng Hu; Steve Bandula; Dean C. Barratt

doi:10.1117/12.2580712

15 February 2021 End-to-end forecasting of needle trajectory in percutaneous ablation

Mark A. Pinnock, Yipeng Hu, Steve Bandula, Dean C. Barratt

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 1159827 (2021) https://doi.org/10.1117/12.2580712
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Percutaneous techniques are becoming more widely adopted for treating solid organ tumours. With many of these techniques using image guidance, accuracy is essential to reduce the risk of complications and tumour recurrence. We propose a novel approach to needle trajectory forecasting using a 3D U-Net trained on interventional computed tomography (iCT) images from renal cryoablation procedures. The U-Net is trained to predict future needle locations from present iCT images, supervised by ground truth labels at future time points. Furthermore, we demonstrate that forecasting needle trajectory may be substantially improved by Monte Carlo dropout (MCDO), in addition to generating uncertainty maps of the predictions. With 122 training iCT volumes, a Dice coefficient of 0.48 on highly elongated needle morphology was achieved based on 41 unseen test cases, significantly outperforming two models not using MCDO at inference with Dice scores of 0.14 and 0.32. MCDO also greatly improved the predicted needle morphology and was able to incorporate directional information into the predictions. Our approach shows promise for improving accuracy and workflow in image-guided procedures, with an interesting research direction to predict uncertain future interventional events by ensemble in supervised approaches.

Conference Presentation

Citation Download Citation

Mark A. Pinnock, Yipeng Hu, Steve Bandula, and Dean C. Barratt "End-to-end forecasting of needle trajectory in percutaneous ablation", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 1159827 (15 February 2021); https://doi.org/10.1117/12.2580712

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