Despite their effectiveness, current deep learning models face challenges
with images coming from different domains with varying appearance and content.
We introduce SegCLR, a versatile framework designed to segment volumetric
images across different domains, employing supervised and contrastive learning
simultaneously to effectively learn from both labeled and unlabeled data. We
demonstrate the superior performance of SegCLR through a comprehensive
evaluation involving three diverse clinical datasets of retinal fluid
segmentation in 3D Optical Coherence Tomography (OCT), various network
configurations, and verification across 10 different network initializations.
In an unsupervised domain adaptation context, SegCLR achieves results on par
with a supervised upper-bound model trained on the intended target domain.
Notably, we discover that the segmentation performance of SegCLR framework is
marginally impacted by the abundance of unlabeled data from the target domain,
thereby we also propose an effective zero-shot domain adaptation extension of
SegCLR, eliminating the need for any target domain information. This shows that
our proposed addition of contrastive loss in standard supervised training for
segmentation leads to superior models, inherently more generalizable to both
in- and out-of-domain test data. We additionally propose a pragmatic solution
for SegCLR deployment in realistic scenarios with multiple domains containing
labeled data. Accordingly, our framework pushes the boundaries of deep-learning
based segmentation in multi-domain applications, regardless of data
availability - labeled, unlabeled, or nonexistent.

SegCLR 是一个多功能的框架，旨在通过同时采用监督学习和对比学习，跨不同领域对体积图像进行分割，有效地从标记和无标记数据中学习。