  Lateral ventricular volume (LVV) is an important biomarker for clinical
investigation. We present the first transfer learning-based LVV segmentation
method for fluid-attenuated inversion recovery (FLAIR) MRI. To mitigate
covariate shifts between source and target domains, this work proposes an
domain adaptation method that optimizes performance on three target datasets.
Silver standard (SS) masks were generated from the target domain using a novel
conventional image processing ventricular segmentation algorithm and used to
supplement the gold standard (GS) data from the source domain, Canadian
Atherosclerosis Imaging Network (CAIN). Four models were tested on held-out
test sets from four datasets: 1) SS+GS: trained on target SS masks and
fine-tuned on source GS masks, 2) GS+SS: trained on source GS masks and
fine-tuned on target SS masks, 3) trained on source GS (GS CAIN Only) and 4)
trained on target SS masks (SS Only). The SS+GS model had the best and most
consistent performance (mean DSC = 0.89, CoV = 0.05) and showed significantly
(p < 0.05) higher DSC compared to the GS-only model on three target domains.
Results suggest pre-training with noisy labels from the target domain allows
the model to adapt to the dataset-specific characteristics and provides robust
parameter initialization while fine-tuning with GS masks allows the model to
learn detailed features. This method has wide application to other medical
imaging problems where labeled data is scarce, and can be used as a per-dataset
calibration method to accelerate wide-scale adoption.
