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Abstract #0972

Real-Time Needle Detection and Segmentation using Mask R-CNN for MRI-Guided Interventions.

Xinzhou Li1,2, Steven S. Raman1, David Lu1, Yu-Hsiu Lee3, Tsu-Chin Tsao3, and Holden H. Wu1

1Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, 2Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, 3Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, CA, United States

Real-time needle tracking for MRI-guided interventions is challenging due to variations in the needle features and the contrast between the needle and surrounding tissue. Mask region-based convolutional neural network (R-CNN) is a powerful deep-learning technique for object detection and segmentation in natural images, which has the potential to overcome these challenges. In this study, we train the Mask R-CNN model using annotated intra-procedural images from MRI-guided prostate biopsy cases and real-time images from MRI-guided needle insertion in a phantom. Mask R-CNN achieved accurate needle detection and segmentation in real time (~80 ms/image), which has the potential to improve MRI-guided interventions.

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