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Automatic Needle Segmentation and Localization in MRI with 3D Convolutional Neural Networks: Application to MRI-targeted Prostate Biopsy

1Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA.
2Tom Tom, Amsterdam, The Netherlands.
3Department of Bioengineering, Imperial College London, UK.
4Department of Electrical and Computer Engineering, The University of British Columbia Vancouver, BC, Canada.
IEEE Engineering in Medicine and Biology Society
Publication Date:
IEEE Trans Med Imaging
Volume Number:
Issue Number:
IEEE Trans Med Imaging. 2019 Apr;38(4):1026-36.
PubMed ID:
Needles, Magnetic resonance imaging, Biopsy, Trajectory, Image segmentation, Cancer, Observers
Appears in Collections:
P41 EB015898/EB/NIBIB NIH HHS/United States
R25 CA089017/CA/NCI NIH HHS/United States
Generated Citation:
Mehrtash A., Ghafoorian M., Pernelle G., Ziaei A., Heslinga F.G., Tuncali K., Fedorov A., Kikinis R., Tempany C.M., Wells III W.M., Abolmaesumi P., Kapur T. Automatic Needle Segmentation and Localization in MRI with 3D Convolutional Neural Networks: Application to MRI-targeted Prostate Biopsy. IEEE Trans Med Imaging. 2019 Apr;38(4):1026-36. PMID: 30334789. PMCID: PMC6450731.
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Image-guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical research program in prostate biopsy. To the best of our knowledge, this is the first reported system for this clinical application, and also the first reported system that leverages deep neural networks for segmentation and localization of needles in MRI across biomedical applications. Needle tip and trajectory were annotated on 583 T2-weighted intra-procedural MRI scans acquired after needle insertion for 71 patients who underwent transperenial MRI-targeted biopsy procedure at our institution. The images were divided into two independent training-validation and test sets at the patient level. A deep 3-dimensional fully convolutional neural network model was developed, trained and deployed on these samples. The accuracy of the proposed method, as tested on previously unseen data, was 2.80 mm average in needle tip detection, and 0.98° in needle trajectory angle. An observer study was designed in which independent annotations by a second observer, blinded to the original observer, were compared to the output of the proposed method. The resultant error was comparable to the measured inter-observer concordance, reinforcing the clinical acceptability of the proposed method. The proposed system has the potential for deployment in clinical routine.