Automatic needle tracking using Mask R-CNN for MRI-guided percutaneous interventions
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ORIGINAL ARTICLE
Automatic needle tracking using Mask R‑CNN for MRI‑guided percutaneous interventions Xinzhou Li1,2 · Adam S. Young1 · Steven S. Raman1 · David S. Lu1 · Yu‑Hsiu Lee3 · Tsu‑Chin Tsao3 · Holden H. Wu1,2 Received: 27 November 2019 / Accepted: 3 July 2020 © CARS 2020
Abstract Purpose Accurate needle tracking provides essential information for MRI-guided percutaneous interventions. Passive needle tracking using MR images is challenged by variations of the needle-induced signal void feature in different situations. This work aimed to develop an automatic needle tracking algorithm for MRI-guided interventions based on the Mask Region Proposal-Based Convolutional Neural Network (R-CNN). Methods Mask R-CNN was adapted and trained to segment the needle feature using 250 intra-procedural images from 85 MRI-guided prostate biopsy cases and 180 real-time images from MRI-guided needle insertion in ex vivo tissue. The segmentation masks were passed into the needle feature localization algorithm to extract the needle feature tip location and axis orientation. The proposed algorithm was tested using 208 intra-procedural images from 40 MRI-guided prostate biopsy cases, and 3 real-time MRI datasets in ex vivo tissue. The algorithm results were compared with human-annotated references. Results In prostate datasets, the proposed algorithm achieved needle feature tip localization error with median Euclidean distance (dxy) of 0.71 mm and median difference in axis orientation angle (dθ) of 1.28°, respectively. In 3 real-time MRI datasets, the proposed algorithm achieved consistent dynamic needle feature tracking performance with processing time of 75 ms/image: (a) median dxy = 0.90 mm, median dθ = 1.53°; (b) median dxy = 1.31 mm, median dθ = 1.9°; (c) median dxy = 1.09 mm, median dθ = 0.91°. Conclusions The proposed algorithm using Mask R-CNN can accurately track the needle feature tip and axis on MR images from in vivo intra-procedural prostate biopsy cases and ex vivo real-time MRI experiments with a range of different conditions. The algorithm achieved pixel-level tracking accuracy in real time and has potential to assist MRI-guided percutaneous interventions. Keywords Interventional MRI · Device tracking · Needle feature · Real-time MRI · Deep learning · Convolutional neural network
Introduction Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11548-020-02226-8) contains supplementary material, which is available to authorized users. * Holden H. Wu [email protected] 1
Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095, USA
2
Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
3
Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Los Angeles, CA, USA
Image-guided percutaneous interventions play key roles in cancer diagnosis and treatment [1, 2]. Under image guidance, physicians manipula
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