  We propose Video-TransUNet, a deep architecture for instance segmentation in
medical CT videos constructed by integrating temporal feature blending into the
TransUNet deep learning framework. In particular, our approach amalgamates
strong frame representation via a ResNet CNN backbone, multi-frame feature
blending via a Temporal Context Module (TCM), non-local attention via a Vision
Transformer, and reconstructive capabilities for multiple targets via a
UNet-based convolutional-deconvolutional architecture with multiple heads. We
show that this new network design can significantly outperform other
state-of-the-art systems when tested on the segmentation of bolus and
pharynx/larynx in Videofluoroscopic Swallowing Study (VFSS) CT sequences. On
our VFSS2022 dataset it achieves a dice coefficient of 0.8796 and an average
surface distance of 1.0379 pixels. Note that tracking the pharyngeal bolus
accurately is a particularly important application in clinical practice since
it constitutes the primary method for diagnostics of swallowing impairment. Our
findings suggest that the proposed model can indeed enhance the TransUNet
architecture via exploiting temporal information and improving segmentation
performance by a significant margin. We publish key source code, network
weights, and ground truth annotations for simplified performance reproduction.
