Papers › Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation

Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation

22 Feb 2023arXiv:2302.11325archive 2025-07-28

Chengxi Zeng, Xinyu Yang, David Smithard, Majid Mirmehdi, Alberto M Gambaruto, Tilo Burghardt

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.

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Decoder Image Segmentation Instance Segmentation Medical Image Segmentation Segmentation Semantic Segmentation Video Segmentation Video Semantic Segmentation

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Absolute Position Encodings Adam Attention BPE Convolution Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Stochastic Depth Swin Transformer Transformer

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