Deep learning for guidewire detection in intravascular ultrasound images

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.

Subjects

Coronary artery

Heatmap

Multi-task learning

Regularization

Segmentation

Vessel

DDC Class

600: Technik

610: Medizin

Funding(s)

MALEKA: Maschinelle Lernverfahren für die kardiovaskuläre Bildgebung auf der Grundlage des Programms für Innovation (PROFI) - Modul PROFI Transfer Plus

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

10.1515_cdbme-2021-1023.pdf

Size

1.29 MB

Format

Adobe PDF

Options

Deep learning for guidewire detection in intravascular ultrasound images