Automatic needle tracking using Mask R-CNN for MRI-guided percutaneous interventions

• Published in: International journal for computer assisted radiology and surgery Vol. 15; no. 10; pp. 1673 - 1684
• Main Authors: Li, Xinzhou, Young, Adam S., Raman, Steven S., Lu, David S., Lee, Yu-Hsiu, Tsao, Tsu-Chin, Wu, Holden H.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.10.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Biopsy; Computer Imaging; Computer Science; Datasets; Euclidean geometry; Feature extraction; Health Informatics; Humans; Image segmentation; Image-Guided Biopsy - methods; Imaging; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medicine; Medicine & Public Health; Needles; Neural Networks, Computer; Original Article; Pattern Recognition and Graphics; Percutaneous treatment; Prostate; Prostate - pathology; Radiology; Real time; Surgery; Tracking; Vision; Deep learning; Needle feature; Interventional MRI; Device tracking; Convolutional neural network; Real-time MRI

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 al...