  Deep learning algorithms utilizing magnetic resonance (MR) images have
demonstrated cutting-edge proficiency in autonomously segmenting multiple
sclerosis (MS) lesions. Despite their achievements, these algorithms may
struggle to extend their performance across various sites or scanners, leading
to domain generalization errors. While few-shot or one-shot domain adaptation
emerges as a potential solution to mitigate generalization errors, its efficacy
might be hindered by the scarcity of labeled data in the target domain. This
paper seeks to tackle this challenge by integrating one-shot adaptation data
with harmonized training data that incorporates labels. Our approach involves
synthesizing new training data with a contrast akin to that of the test domain,
a process we refer to as "contrast harmonization" in MRI. Our experiments
illustrate that the amalgamation of one-shot adaptation data with harmonized
training data surpasses the performance of utilizing either data source in
isolation. Notably, domain adaptation using exclusively harmonized training
data achieved comparable or even superior performance compared to one-shot
adaptation. Moreover, all adaptations required only minimal fine-tuning,
ranging from 2 to 5 epochs for convergence.
