运动校正移动平均：引入事后时序信息以改进视频分割
Motion-Corrected Moving Average: Including Post-Hoc Temporal Information for Improved Video Segmentation
AI总结：
针对医学视频分割的帧间预测噪声问题，提出无需修改训练或额外标注的运动校正移动平均方法，用光流对齐先验项提升性能，且无显著运行损耗，在多数据集上优于基线。
摘要：
实时计算速度与高精度是计算机辅助干预的必备要求。将分割网络应用于医学视频处理任务时，会产生显著的帧间预测噪声。现有方法可通过引入时序信息减少不一致性，但通常对网络架构或数据集有特定要求。本文提出一种可在任意分割模型中引入时序信息的方法，由此实现无需修改训练过程、无需额外标注即可提升视频分割性能的技术。借助运动校正移动平均（Motion-Corrected Moving Average），我们对当前预测与先前预测之间的指数移动平均进行优化。通过光流估计连续帧之间的运动，我们可平移移动平均计算中的先验项，使其与当前帧的几何结构对齐。光流计算无需依赖模型输出，因此可并行执行，不会给我们的方法带来显著的运行时间损耗。我们在两个公开可用的分割数据集和两个专有内窥镜数据集上评估了该方法，结果显示其性能优于基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
