Deep learning-based automatic pipeline for 3D needle localization on intra-procedural 3D MRI.

Journal: International journal of computer assisted radiology and surgery
Published Date: Mar 23, 2024

Abstract

PURPOSE: Accurate and rapid needle localization on 3D magnetic resonance imaging (MRI) is critical for MRI-guided percutaneous interventions. The current workflow requires manual needle localization on 3D MRI, which is time-consuming and cumbersome. Automatic methods using 2D deep learning networks for needle segmentation require manual image plane localization, while 3D networks are challenged by the need for sufficient training datasets. This work aimed to develop an automatic deep learning-based pipeline for accurate and rapid 3D needle localization on in vivo intra-procedural 3D MRI using a limited training dataset.

Authors

  • Wenqi Zhou
    Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.
  • Xinzhou Li
    Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.
  • Fatemeh Zabihollahy
    Department of Systems and Computer Engineering, Carleton University, 339 Riversedge Crescent, Ottawa, ON, K1V 0Y6, Canada. fatemehzabihollahy@cmail.carleton.ca.
  • David S Lu
    Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.
  • Holden H Wu
    Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.

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