Learning needle tip localization from digital subtraction in 2D ultrasound

Cosmas Mwikirize, John L. Nosher, Ilker Hacihaliloglu

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Purpose: This paper addresses localization of needles inserted both in-plane and out-of-plane in challenging ultrasound-guided interventions where the shaft and tip have low intensity. Our approach combines a novel digital subtraction scheme for enhancement of low-level intensity changes caused by tip movement in the ultrasound image and a state-of-the-art deep learning scheme for tip detection. Methods: As the needle tip moves through tissue, it causes subtle spatiotemporal variations in intensity. Relying on these intensity changes, we formulate a foreground detection scheme for enhancing the tip from consecutive ultrasound frames. The tip is augmented by solving a spatial total variation regularization problem using the split Bregman method. Lastly, we filter irrelevant motion events with a deep learning-based end-to-end data-driven method that models the appearance of the needle tip in ultrasound images, resulting in needle tip detection. Results: The detection model is trained and evaluated on an extensive ex vivo dataset collected with 17G and 22G needles inserted in-plane and out-of-plane in bovine, porcine and chicken phantoms. We use 5000 images extracted from 20 video sequences for training and 1000 images from 10 sequences for validation. The overall framework is evaluated on 700 images from 20 sequences not used in training and validation, and achieves a tip localization error of 0.72 ± 0.04 mm and an overall processing time of 0.094 s per frame (~ 10 frames per second). Conclusion: The proposed method is faster and more accurate than state of the art and is resilient to spatiotemporal redundancies. The promising results demonstrate its potential for accurate needle localization in challenging ultrasound-guided interventions.

Original languageAmerican English
JournalInternational Journal of Computer Assisted Radiology and Surgery
StatePublished - Jan 1 2019

ASJC Scopus subject areas

  • Surgery
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


  • Deep learning
  • Minimally invasive procedures
  • Needle tip localization
  • Ultrasound


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