Abstract #0972

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

Xinzhou Li^1,2, 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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