Improving catheter segmentation & location in 3D cardiac ultrasound using direction-fused fcn

H. Yang, C. Shan, Alexander F. Kolen, P.H.N. de With

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

7 Citations (Scopus)
62 Downloads (Pure)


Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention. However, the low image quality of US requires extra training for sonographers to localize the catheter. In this paper, we propose a catheter detection method based on a pre-trained VGG network, which exploits 3D information through re-organized cross-sections to segment the catheter by a shared fully convolutional network (FCN), which is called a Direction-Fused FCN (DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized by model fitting. Our experiments show that the proposed method can successfully detect an ablation catheter in a challenging ex-vivo 3D US dataset, which was collected on the porcine heart. Extensive analysis shows that the proposed method achieves a Dice score of 57.7%, which offers at least an 11.8% improvement when compared to state-of the-art instrument detection methods. Due to the improved segmentation performance by the DF-FCN, the catheter can be localized with an error of only 1.4 mm.

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)978-1-5386-3641-1
Publication statusPublished - Apr 2019
EventInternational Symposium on Biomedical Imaging 2019 - Venice, Italy
Duration: 8 Apr 201911 Apr 2019


ConferenceInternational Symposium on Biomedical Imaging 2019
Internet address


  • 3D ultrasound
  • Catheter segmentation localization VGG pre-trained model
  • Fine-tuning


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