ConTrack: Contextual Transformer for Device Tracking in X-ray
Marc Demoustier Yue Zhang Venkatesh Narasimha Murthy Florin C. Ghesu Dorin Comaniciu
Device tracking is an important prerequisite for guidance during endovascular
procedures. Especially during cardiac interventions, detection and tracking of
guiding the catheter tip in 2D fluoroscopic images is important for
applications such as mapping vessels from angiography (high dose with contrast)
to fluoroscopy (low dose without contrast). Tracking the catheter tip poses
different challenges: the tip can be occluded by contrast during angiography or
interventional devices; and it is always in continuous movement due to the
cardiac and respiratory motions. To overcome these challenges, we propose
ConTrack, a transformer-based network that uses both spatial and temporal
contextual information for accurate device detection and tracking in both X-ray
fluoroscopy and angiography. The spatial information comes from the template
frames and the segmentation module: the template frames define the surroundings
of the device, whereas the segmentation module detects the entire device to
bring more context for the tip prediction. Using multiple templates makes the
model more robust to the change in appearance of the device when it is occluded
by the contrast agent. The flow information computed on the segmented catheter
mask between the current and the previous frame helps in further refining the
prediction by compensating for the respiratory and cardiac motions. The
experiments show that our method achieves 45% or higher accuracy in detection
and tracking when compared to state-of-the-art tracking models.