Improving needle detection in 3D ultrasound using orthogonal-plane convolutional networks

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Abstract

Successful automated detection of short needles during an intervention is necessary to allow the physician identify and correct any misalignment of the needle and the target at early stages, which reduces needle passes and improves health outcomes. In this paper, we present a novel approach to detect needle voxels in 3D ultrasound volume with high precision using convolutional neural networks. Each voxel is classified from locally-extracted raw data of three orthogonal planes centered on it. We propose a bootstrap re-sampling approach to enhance the training in our highly imbalanced data. The proposed method successfully detects 17G and 22G needles with a single trained network, showing a robust generalized approach. Extensive ex-vivo evaluations on 3D ultrasound datasets of chicken breast show 25% increase in F1-score over the state-of-the-art feature-based method. Furthermore, very short needles inserted for only 5 mm in the volume are detected with tip localization errors of <0.5 mm, indicating that the tip is always visible in the detected plane.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention − MICCAI 2017
Subtitle of host publication20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II
EditorsPierre Jannin, Simon Duchesne, Maxime Descoteaux, Alfred Franz, D. Louis Collins, Lena Maier-Hein
Place of PublicationDordrecht
PublisherSpringer
Pages610-618
Number of pages9
ISBN (Electronic)978-3-319-66185-8
ISBN (Print)978-3-319-66184-1
DOIs
Publication statusPublished - 4 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10434 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • 3D ultrasound
  • Convolutional networks
  • Needle detection

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