  Medical image annotation typically requires expert knowledge and hence incurs
time-consuming and expensive data annotation costs. To alleviate this burden,
we propose a novel learning scenario, Exemplar Learning (EL), to explore
automated learning processes for medical image segmentation with a single
annotated image example. This innovative learning task is particularly suitable
for medical image segmentation, where all categories of organs can be presented
in one single image and annotated all at once. To address this challenging EL
task, we propose an Exemplar Learning-based Synthesis Net (ELSNet) framework
for medical image segmentation that enables innovative exemplar-based data
synthesis, pixel-prototype based contrastive embedding learning, and
pseudo-label based exploitation of the unlabeled data. Specifically, ELSNet
introduces two new modules for image segmentation: an exemplar-guided synthesis
module, which enriches and diversifies the training set by synthesizing
annotated samples from the given exemplar, and a pixel-prototype based
contrastive embedding module, which enhances the discriminative capacity of the
base segmentation model via contrastive representation learning. Moreover, we
deploy a two-stage process for segmentation model training, which exploits the
unlabeled data with predicted pseudo segmentation labels. To evaluate this new
learning framework, we conduct extensive experiments on several organ
segmentation datasets and present an in-depth analysis. The empirical results
show that the proposed exemplar learning framework produces effective
segmentation results.
