  This paper presents a deep learning framework for medical video segmentation.
Convolution neural network (CNN) and transformer-based methods have achieved
great milestones in medical image segmentation tasks due to their incredible
semantic feature encoding and global information comprehension abilities.
However, most existing approaches ignore a salient aspect of medical video data
- the temporal dimension. Our proposed framework explicitly extracts features
from neighbouring frames across the temporal dimension and incorporates them
with a temporal feature blender, which then tokenises the high-level
spatio-temporal feature to form a strong global feature encoded via a Swin
Transformer. The final segmentation results are produced via a UNet-like
encoder-decoder architecture. Our model outperforms other approaches by a
significant margin and improves the segmentation benchmarks on the VFSS2022
dataset, achieving a dice coefficient of 0.8986 and 0.8186 for the two datasets
tested. Our studies also show the efficacy of the temporal feature blending
scheme and cross-dataset transferability of learned capabilities. Code and
models are fully available at https://github.com/SimonZeng7108/Video-SwinUNet.
