ArticleIEEE transactions on medical imaging2019
Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRI-Targeted Prostate Biopsy.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 64 citations in OpenAlex.
- A Review on Artificial Intelligence as a Solution to Burnout in Interventional Radiology.Cardiovascular and interventional radiology · 2026Review
- Keypoint detection network for needle localization on intra-procedural MRI in MRI-guided liver interventions.International journal of computer assisted radiology and surgery · 2026Article
- Computer-assisted Treatment Planning and Mathematical Modeling for Percutaneous Liver Tumor Ablation: An Updated Survey.Annals of biomedical engineering · 2026Review
- FM-Adapt: Foundation model adaptation with photoacoustic-supervised learning for interventional ultrasound.Photoacoustics · 2026Article
- Image-Guided Adaptive Cryotherapy for Prostate Cancer Treatment.Annals of biomedical engineering · 2025Article
- Hybrid pneumatic-hydraulic actuation for MRI-guided robotic stereotactic neurointervention.Science advances · 2025Article
- Deep learning-based temporal MR image reconstruction for accelerated interventional imaging during in-bore biopsies.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Deep learning-based automatic pipeline for 3D needle localization on intra-procedural 3D MRI.International journal of computer assisted radiology and surgery · 2024Article
- Artificial intelligence in interventional radiology: state of the art.European radiology experimental · 2024Review
- Two-stage generative adversarial networks for metal artifact reduction and visualization in ablation therapy of liver tumors.International journal of computer assisted radiology and surgery · 2023Article
- AI-Based Isotherm Prediction for Focal Cryoablation of Prostate Cancer.Academic radiology · 2023Article
- Simultaneous Online Registration-Independent Stiffness Identification and Tip Localization of Surgical Instruments in Robot-assisted Eye Surgery.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2023Article
- Automatic segmentation of prostate and extracapsular structures in MRI to predict needle deflection in percutaneous prostate intervention.International journal of computer assisted radiology and surgery · 2023Article
- Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming.Frontiers in cardiovascular medicine · 2023Article
- Deep-learning-assisted algorithm for catheter reconstruction during MR-only gynecological interstitial brachytherapy.Journal of applied clinical medical physics · 2022Article
- Concurrently bendable and rotatable continuum tubular robot for omnidirectional multi-core transurethral prostate biopsy.Medical & biological engineering & computing · 2022Article
- Automated segmentation of biventricular contours in tissue phase mapping using deep learning.NMR in biomedicine · 2021Article
- Domain adaptation for segmentation of critical structures for prostate cancer therapy.Scientific reports · 2021Article
- Role of Machine Learning and Artificial Intelligence in Interventional Oncology.Current oncology reports · 2021Review
- Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation.IEEE transactions on medical imaging · 2020Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 4 countries.
Funding
Abstract
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 transperineal 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-D 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 with 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.