Real-time computational speed and a high degree of precision are requirements
for computer-assisted interventions. Applying a segmentation network to a
medical video processing task can introduce significant inter-frame prediction
noise. Existing approaches can reduce inconsistencies by including temporal
information but often impose requirements on the architecture or dataset. This
paper proposes a method to include temporal information in any segmentation
model and, thus, a technique to improve video segmentation performance without
alterations during training or additional labeling. With Motion-Corrected
Moving Average, we refine the exponential moving average between the current
and previous predictions. Using optical flow to estimate the movement between
consecutive frames, we can shift the prior term in the moving-average
calculation to align with the geometry of the current frame. The optical flow
calculation does not require the output of the model and can therefore be
performed in parallel, leading to no significant runtime penalty for our
approach. We evaluate our approach on two publicly available segmentation
datasets and two proprietary endoscopic datasets and show improvements over a
baseline approach.

本论文提出一种在任何分割模型中引入时间信息的方法，从而提高视频分割性能，无需训练过程中的改动或额外标注，并通过使用光流计算进行运动校正的移动平均方法，在公开数据集和内部内窥镜数据集上展示了改进。