Single Shot Needle Tip Localization in 2D Ultrasound

Cosmas Mwikirize, John L. Nosher, Ilker Hacihaliloglu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We present a novel real-time technique for dynamic localization of the needle tip in 2D ultrasound during challenging interventions in which the tip is imperceptible or shaft information is unavailable. We first enhance the needle tip from time-series ultrasound data through digital subtraction of consecutive frames. The enhanced tip image is then fed to a cascade of similar convolutional neural networks: a tip classifier and a tip location regressor. The classifier ascertains tip motion and the regressor directly outputs the coordinates of the tip. Since we do not require needle shaft information, the method achieves efficient localization of both in-plane and out-of-plane needles. Our approach is trained and evaluated on an ex vivo dataset collected using two different ultrasound machines, with in-plane and out-of-plane insertion of 17G and 22G needles in bovine, porcine and chicken tissue. We use 12, 000 frames extracted from 40 video sequences for training and validation, and 500 frames from 20 sequences as test data. The framework achieves a tip localization error of 0.55\0.07mm, and overall processing time of 0.015 s (67 fps). Validation studies against state-of-the-art achieved and improvement in accuracy and processing rate respectively. Because of the real-time execution time and accurate tip localization, we believe that our approach is potentially a breakthrough for real-time needle tip localization in challenging ultrasound-guided interventions.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer
Pages637-645
Number of pages9
ISBN (Print)9783030322533
DOIs
StatePublished - Jan 1 2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 13 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11768 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/TerritoryChina
CityShenzhen
Period10/13/1910/17/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Deep learning
  • Needle enhancement
  • Needle localization
  • Ultrasound

Fingerprint

Dive into the research topics of 'Single Shot Needle Tip Localization in 2D Ultrasound'. Together they form a unique fingerprint.

Cite this