Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3826
Publisher DOI: 10.1515/cdbme-2021-1023
Title: Deep learning for guidewire detection in intravascular ultrasound images
Language: English
Authors: Bargsten, Lennart 
Klisch, Daniel 
Riedl, Katharina Alina 
Wissel, Tobias 
Brunner, Fabian J. 
Schaefers, Klaus 
Graß, Michael 
Blankenberg, Stefan 
Seiffert, Moritz 
Schlaefer, Alexander 
Keywords: Coronary artery;Heatmap;Multi-task learning;Regularization;Segmentation;Vessel
Issue Date: 1-Aug-2021
Publisher: De Gruyter
Source: Current Directions in Biomedical Engineering 7 (1): 20211125, 106-110 (2021-08-01)
Journal: Current directions in biomedical engineering 
Abstract (english): 
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.
URI: http://hdl.handle.net/11420/10495
DOI: 10.15480/882.3826
ISSN: 2364-5504
Institute: Medizintechnische und Intelligente Systeme E-1 
Document Type: Article
Project: MALEKA: Maschinelle Lernverfahren für die kardiovaskuläre Bildgebung auf der Grundlage des Programms für Innovation (PROFI) - Modul PROFI Transfer Plus 
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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