Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI
Journal:
arXiv
Published Date:
May 30, 2025
Abstract
Accurate placental segmentation is essential for quantitative analysis of the
placenta. However, this task is particularly challenging in T2*-weighted
placental imaging due to: (1) weak and inconsistent boundary contrast across
individual echoes; (2) the absence of manual ground truth annotations for all
echo times; and (3) motion artifacts across echoes caused by fetal and maternal
movement. In this work, we propose a contrast-augmented segmentation framework
that leverages complementary information across multi-echo T2*-weighted MRI to
learn robust, contrast-invariant representations. Our method integrates: (i)
masked autoencoding (MAE) for self-supervised pretraining on unlabeled
multi-echo slices; (ii) masked pseudo-labeling (MPL) for unsupervised domain
adaptation across echo times; and (iii) global-local collaboration to align
fine-grained features with global anatomical context. We further introduce a
semantic matching loss to encourage representation consistency across echoes of
the same subject. Experiments on a clinical multi-echo placental MRI dataset
demonstrate that our approach generalizes effectively across echo times and
outperforms both single-echo and naive fusion baselines. To our knowledge, this
is the first work to systematically exploit multi-echo T2*-weighted MRI for
placental segmentation.
