Deep learning for guidewire detection in intravascular ultrasound images

Abstract: Algorithms for automated analysis of intravascular ultrasound (IVUS) images can be disturbed by guidewires, which are often encountered when treating bifurcations in percutaneous coronary interventions. Detecting guidewires in advance can therefore help avoiding potential errors. This task is not trivial, since guidewires appear rather small compared to other relevant objects in IVUS images. We employed CNNs with additional multi-task learning as well as different guidewire-specific regularizations to enable and improve guidewire detection. In this context, we developed a network block which generates heatmaps that highlight guidewires without the need of localization annotations. The guidewire detection results reach values of 0.931 in terms of the F1-score and 0.996 in terms of area under curve (AUC). Comparing thresholded guidewire heatmaps with ground truth segmentation masks leads to a Dice score of 23.1 % and an average Hausdorff distance of 1.45 mm. Guidewire detection has proven to be a task that CNNs can handle quite well. Employing multi-task learning and guidewire-specific regularizations further improve detection results and enable generation of heatmaps that indicate the position of guidewires without actual labels.

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch

Bibliographic citation
Deep learning for guidewire detection in intravascular ultrasound images ; volume:7 ; number:1 ; year:2021 ; pages:106-110 ; extent:5
Current directions in biomedical engineering ; 7, Heft 1 (2021), 106-110 (gesamt 5)

Creator
Holstein, Lennart
Klisch, Daniel
Riedl, Katharina A.
Wissel, Tobias
Brunner, Fabian J.
Schaefers, Klaus
Graß, Michael
Blankenberg, Stefan
Seiffert, Moritz
Schlaefer, Alexander

DOI
10.1515/cdbme-2021-1023
URN
urn:nbn:de:101:1-2410141713442.770432986307
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
15.08.2025, 7:32 AM CEST

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