Motion-Corrected Moving Average: Including Post-Hoc Temporal Information
  for Improved Video Segmentation
Robert Mendel Tobias Rueckert Dirk Wilhelm Daniel Rueckert Christoph Palm
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.