  Downsampling images and labels, often necessitated by limited resources or to
expedite network training, leads to the loss of small objects and thin
boundaries. This undermines the segmentation network's capacity to interpret
images accurately and predict detailed labels, resulting in diminished
performance compared to processing at original resolutions. This situation
exemplifies the trade-off between efficiency and accuracy, with higher
downsampling factors further impairing segmentation outcomes. Preserving
information during downsampling is especially critical for medical image
segmentation tasks. To tackle this challenge, we introduce a novel method named
Edge-preserving Probabilistic Downsampling (EPD). It utilizes class uncertainty
within a local window to produce soft labels, with the window size dictating
the downsampling factor. This enables a network to produce quality predictions
at low resolutions. Beyond preserving edge details more effectively than
conventional nearest-neighbor downsampling, employing a similar algorithm for
images, it surpasses bilinear interpolation in image downsampling, enhancing
overall performance. Our method significantly improved Intersection over Union
(IoU) to 2.85%, 8.65%, and 11.89% when downsampling data to 1/2, 1/4, and 1/8,
respectively, compared to conventional interpolation methods.
