Deep learning for guidewire detection in intravascular ultrasound images
- Resource Type
- Authors
- Alexander Schlaefer; Michael Grass; Moritz Seiffert; Klaus Schaefers; Katharina A. Riedl; Fabian J. Brunner; Tobias Wissel; Lennart Bargsten; Stefan Blankenberg; Daniel Klisch
- Source
- Current Directions in Biomedical Engineering, Vol 7, Iss 1, Pp 106-110 (2021)
Current Directions in Biomedical Engineering 7 (1): 20211125 (2021-08-01)
- Subject
- Vessel
coronary artery
heatmap
Biomedical Engineering
vessel
multi-task learning
Coronary artery
Segmentation
Regularization
Intravascular ultrasound
Medicine
ddc:610
Heatmap
Technik [600]
medicine.diagnostic_test
business.industry
Deep learning
600: Technik
segmentation
regularization
Medizin [610]
Multi-task learning
610: Medizin
Artificial intelligence
business
ddc:600
Biomedical engineering
- Language
- English
- ISSN
- 2364-5504
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.