  MRI scans appearance significantly depends on scanning protocols and,
consequently, the data-collection institution. These variations between
clinical sites result in dramatic drops of CNN segmentation quality on unseen
domains. Many of the recently proposed MRI domain adaptation methods operate
with the last CNN layers to suppress domain shift. At the same time, the core
manifestation of MRI variability is a considerable diversity of image
intensities. We hypothesize that these differences can be eliminated by
modifying the first layers rather than the last ones. To validate this simple
idea, we conducted a set of experiments with brain MRI scans from six domains.
Our results demonstrate that 1) domain-shift may deteriorate the quality even
for a simple brain extraction segmentation task (surface Dice Score drops from
0.85-0.89 even to 0.09); 2) fine-tuning of the first layers significantly
outperforms fine-tuning of the last layers in almost all supervised domain
adaptation setups. Moreover, fine-tuning of the first layers is a better
strategy than fine-tuning of the whole network, if the amount of annotated data
from the new domain is strictly limited.
