Deep learning to detect catheter tips in vivo during photoacoustic-guided catheter interventions: Invited Presentation

Derek Allman, Fabrizio Assis, Jonathan Chrispin, Muyinatu A.Lediju Bell

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

Abstract

Catheter guidance is typically performed with fluoroscopy, which requires patient and operator exposure to ionizing radiation. Our group is exploring robotic photoacoustic imaging as an alternative to fluoroscopy to track catheter tips. However, the catheter tip segmentation step in the photoacoustic-based robotic visual servoing algorithm is limited by the presence of confusing photoacoustic artifacts. We previously demonstrated that a deep neural network is capable of detecting photoacoustic sources in the presence of artifacts in simulated, phantom, and in vivo data. This paper directly compares the in vivo results obtained with linear and phased ultrasound receiver arrays. Two convolutional neural networks (CNNs) were trained to detect point sources in simulated photoacoustic channel data and tested with in vivo images from a swine catheterization procedure. The CNN trained with a linear array receiver model correctly classified 88.8% of sources, and the CNN trained with a phased array receiver model correctly classified 91.4% of sources. These results demonstrate that a deep learning approach to photoacoustic image formation is capable of detecting catheter tips during interventional procedures. Therefore, the proposed approach is a promising replacement to the segmentation step in photoacoustic-based robotic visual servoing algorithms.

Original languageEnglish (US)
Title of host publication2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728111513
DOIs
Publication statusPublished - Apr 16 2019
Event53rd Annual Conference on Information Sciences and Systems, CISS 2019 - Baltimore, United States
Duration: Mar 20 2019Mar 22 2019

Publication series

Name2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019

Conference

Conference53rd Annual Conference on Information Sciences and Systems, CISS 2019
CountryUnited States
CityBaltimore
Period3/20/193/22/19

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ASJC Scopus subject areas

  • Information Systems

Cite this

Allman, D., Assis, F., Chrispin, J., & Bell, M. A. L. (2019). Deep learning to detect catheter tips in vivo during photoacoustic-guided catheter interventions: Invited Presentation. In 2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019 [8692864] (2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2019.8692864