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.

翻译：本文提出了一种用于医学视频分割的深度学习框架。卷积神经网络（CNN）和基于Transformer的方法凭借其卓越的语义特征编码与全局信息理解能力，已在医学图像分割任务中取得重大突破。然而，现有方法大多忽略了医学视频数据的关键维度——时间维度。我们提出的框架明确地从相邻帧中沿时间维度提取特征，并通过时间特征融合模块进行整合，随后将高级时空特征进行令牌化处理，形成经由Swin Transformer编码的强全局特征。最终分割结果通过类似UNet的编码器-解码器架构生成。本模型在性能上显著优于其他方法，并在VFSS2022数据集上提升了分割基准：在两个测试数据集上分别达到0.8986和0.8186的Dice系数。我们的研究还验证了时间特征融合方案的有效性以及所学能力的跨数据集迁移性。代码与模型完全公开于https://github.com/SimonZeng7108/Video-SwinUNet。