Landmark constellation models for central venous catheter malposition detection

Ilyas Sirazitdinov, Matthias Lenga, Ivo M. Baltruschat, Dmitry V. Dylov, Axel Saalbach

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

Abstract

The placement of a central venous catheter (CVC) for venous access is a common clinical routine. Nonetheless, various clinical studies report that CVC insertions are unsuccessful in up to 20% of all cases. Among other, typical complications include the incidence of a pneumothorax, hemothorax, arterial puncture, venous air embolism, arrhythmias or catheter knotting. In order to detect the CVC tip in chest X-ray (CXR) images, and to evaluate the catheter placement, we propose a HRNet-based key point detection approach in combination with a probabilistic constellation model. In a cross-validation study, we show that our approach not only enables the exact localization of the CVC tip, but also of relevant anatomical landmarks. Moreover, the probabilistic model provides a likelihood score for tip position which allows us to identify malpositioned CVCs.

Original languageEnglish
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Pages1132-1136
Number of pages5
ISBN (Electronic)9781665412469
DOIs
Publication statusPublished - 13 Apr 2021
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: 13 Apr 202116 Apr 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2021-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Country/TerritoryFrance
CityNice
Period13/04/2116/04/21

Keywords

  • Central venous catheter
  • Constellation model
  • Landmark detection
  • Malposition detection
  • X-ray

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