  Deep learning (DL) methods for white matter lesion (WML) segmentation in MRI
suffer a reduction in performance when applied on data from a scanner or centre
that is out-of-distribution (OOD) from the training data. This is critical for
translation and widescale adoption, since current models cannot be readily
applied to data from new institutions. In this work, we evaluate several
intensity standardization methods for MRI as a preprocessing step for WML
segmentation in multi-centre Fluid-Attenuated Inversion Recovery (FLAIR) MRI.
We evaluate a method specifically developed for FLAIR MRI called IAMLAB along
with other popular normalization techniques such as White-strip, Nyul and
Z-score. We proposed an Ensemble model that combines predictions from each of
these models. A skip-connection UNet (SC UNet) was trained on the standardized
images, as well as the original data and segmentation performance was evaluated
over several dimensions. The training (in-distribution) data consists of a
single study, of 60 volumes, and the test (OOD) data is 128 unseen volumes from
three clinical cohorts. Results show IAMLAB and Ensemble provide higher WML
segmentation performance compared to models from original data or other
normalization methods. IAMLAB & Ensemble have the highest dice similarity
coefficient (DSC) on the in-distribution data (0.78 & 0.80) and on clinical OOD
data. DSC was significantly higher for IAMLAB compared to the original data
(p<0.05) for all lesion categories (LL>25mL: 0.77 vs. 0.71; 10mL<= LL<25mL:
0.66 vs. 0.61; LL<10mL: 0.53 vs. 0.52). The IAMLAB and Ensemble normalization
methods are mitigating MRI domain shift and are optimal for DL-based WML
segmentation in unseen FLAIR data.
