  The impressive growth of data throughput in optical microscopy has triggered
a widespread use of supervised learning (SL) models running on compressed image
datasets for efficient automated analysis. However, since lossy image
compression risks to produce unpredictable artifacts, quantifying the effect of
data compression on SL applications is of pivotal importance to assess their
reliability, especially for clinical use. We propose an experimental method to
evaluate the tolerability of image compression distortions in 2D and 3D cell
segmentation SL tasks: predictions on compressed data are compared to the raw
predictive uncertainty, which is numerically estimated from the raw noise
statistics measured through sensor calibration. We show that predictions on
object- and image-specific segmentation parameters can be altered by up to 15%
and more than 10 standard deviations after 16-to-8 bits downsampling or JPEG
compression. In contrast, a recently developed lossless compression algorithm
provides a prediction spread which is statistically equivalent to that stemming
from raw noise, while providing a compression ratio of up to 10:1. By setting a
lower bound to the SL predictive uncertainty, our technique can be generalized
to validate a variety of data analysis pipelines in SL-assisted fields.
