Feature study on catheter detection in three-dimensional ultrasound

Hongxu Yang, Arash Pourtaherian, Caifeng Shan, Alexander F. Kolen, Peter H.N. de With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)
4 Downloads (Pure)


The usage of three-dimensional ultrasound (3D US) during image-guided interventions for e.g. cardiac catheterization has increased recently. To accurately and consistently detect and track catheters or guidewires in the US image during the intervention, additional training of the sonographer or physician is needed. As a result, image-based catheter detection can be beneficial to the sonographer to interpret the position and orientation of a catheter in the 3D US volume. However, due to the limited spatial resolution of 3D cardiac US and complex anatomical structures inside the heart, image-based catheter detection is challenging. In this paper, we study 3D image features for image-based catheter detection using supervised learning methods. To better describe the catheter in 3D US, we extend the Frangi vesselness feature into a multi-scale Objectness feature and a Hessian element feature, which extract more discriminative information about catheter voxels in a 3D US volume. In addition, we introduce a multi-scale statistical 3D feature to enrich and enhance the information for voxel-based classification. Extensive experiments on several in-vitro and ex-vivo datasets show that our proposed features improve the precision to at least 69% when compared to the traditional multi-scale Frangi features (from 45% to 76% at a high recall rate 75%). As for clinical application, the high accuracy of voxel-based classification enables more robust catheter detection in complex anatomical structures.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsR.J. Webster, B. Fei
Place of PublicationWarrendale
ISBN (Electronic)9781510616417
Publication statusPublished - 14 Mar 2018
EventSPIE Medical Imaging 2018 - Houston, United States
Duration: 10 Feb 201815 Feb 2018

Publication series

NameProceedings of SPIE


ConferenceSPIE Medical Imaging 2018
Country/TerritoryUnited States


  • 3D ultrasound
  • Catheter detection
  • Machine learning
  • Multi-scale feature
  • multi-scale feature
  • machine learning


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