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Figure 5. Comparison of catheter tip detection using the U-Net model and adaptive thresholding. Illustration of raw fluoroscopic
images, groundtruth, and predicted segmentation for the two testing datasets.
set. Additionally, we demonstrated the 3D trajectory of the catheter tip’s movement can be visualized
graphically.
We believe the 3D trajectory analysis performed by this model can be used to analyze a physicians'
performance and/or provide quantitative feedback for training and educational purposes. This work serves
as a proof-of-principle that deep learning can be used for catheter tracking for cardiac interventions,
however, since this article is a technical note, it has several limitations in its current stage, and we believe
these limitations will be the seed for future developments for both our lab others. These limitations include:
