ArticleInternational journal of computer assisted radiology and surgery2018
Convolution neural networks for real-time needle detection and localization in 2D ultrasound.
Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.
- The use of machine learning in transarterial chemoembolisation/transarterial embolisation for patients with intermediate-stage hepatocellular carcinoma: a systematic review.La Radiologia medica · 2025Pooled it
- Feasibility of Deep Learning Segmentation in Anterior Quadratus Lumborum Ultrasound Imaging: Challenges in Identifying the Injection Plane.Journal of imaging informatics in medicine · 2026Article
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- FM-Adapt: Foundation model adaptation with photoacoustic-supervised learning for interventional ultrasound.Photoacoustics · 2026Article
- ConVibNet: needle detection during continuous insertion via frequency-inspired features.International journal of computer assisted radiology and surgery · 2026Article
- Musculoskeletal ultrasound-guided cellular therapy: Current applications and future directions in skeletal muscle regeneration.Iranian journal of basic medical sciences · 2026Review
- Enhancing Tip Detection by Pre-Training with Synthetic Data for Ultrasound-Guided Intervention.Diagnostics (Basel, Switzerland) · 2025Article
- US-Guided Interventional Procedures for Total Hip Arthroplasty.Journal of clinical medicine · 2024Review
- Artificial intelligence in interventional radiology: state of the art.European radiology experimental · 2024Review
- Needle tracking and segmentation in breast ultrasound imaging based on spatio-temporal memory network.Frontiers in oncology · 2024Article
- Robust tip localization under continuous spatial and temporal constraints during 2D ultrasound-guided needle puncture.International journal of computer assisted radiology and surgery · 2023Article
- Ultrasound-guided needle tracking with deep learning: A novel approach with photoacoustic ground truth.Photoacoustics · 2023Article
- Spatiotemporal analysis of speckle dynamics to track invisible needle in ultrasound sequences using convolutional neural networks: a phantom study.International journal of computer assisted radiology and surgery · 2023Article
- Bridging the simulation-to-real gap for AI-based needle and target detection in robot-assisted ultrasound-guided interventions.European radiology experimental · 2023Article
- Artificial intelligence and anesthesia: a narrative review.Annals of translational medicine · 2022Review
- Percutaneous puncture during PCNL: new perspective for the future with virtual imaging guidance.World journal of urology · 2022Review
- Automatic and accurate needle detection in 2D ultrasound during robot-assisted needle insertion process.International journal of computer assisted radiology and surgery · 2022Article
- Interventional oncology update.European journal of radiology open · 2022Article
- Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review.Frontiers in medicine · 2022Article
- Research on Robotic Humanoid Venipuncture Method Based on Biomechanical Model.Journal of intelligent & robotic systems · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe propose a framework for automatic and accurate detection of steeply inserted needles in 2D ultrasound data using convolution neural networks. We demonstrate its application in needle trajectory estimation and tip localization.
methodsOur approach consists of a unified network, comprising a fully convolutional network (FCN) and a fast region-based convolutional neural network (R-CNN). The FCN proposes candidate regions, which are then fed to a fast R-CNN for finer needle detection. We leverage a transfer learning paradigm, where the network weights are initialized by training with non-medical images, and fine-tuned with ex vivo ultrasound scans collected during insertion of a 17G epidural needle into freshly excised porcine and bovine tissue at depth settings up to 9 cm and [Formula: see text]-[Formula: see text] insertion angles. Needle detection results are used to accurately estimate needle trajectory from intensity invariant needle features and perform needle tip localization from an intensity search along the needle trajectory.
resultsOur needle detection model was trained and validated on 2500 ex vivo ultrasound scans. The detection system has a frame rate of 25 fps on a GPU and achieves 99.6% precision, 99.78% recall rate and an [Formula: see text] score of 0.99. Validation for needle localization was performed on 400 scans collected using a different imaging platform, over a bovine/porcine lumbosacral spine phantom. Shaft localization error of [Formula: see text], tip localization error of [Formula: see text] mm, and a total processing time of 0.58 s were achieved.
conclusionThe proposed method is fully automatic and provides robust needle localization results in challenging scanning conditions. The accurate and robust results coupled with real-time detection and sub-second total processing make the proposed method promising in applications for needle detection and localization during challenging minimally invasive ultrasound-guided procedures.
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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.