  Deep neural networks usually require accurate and a large number of
annotations to achieve outstanding performance in medical image segmentation.
One-shot segmentation and weakly-supervised learning are promising research
directions that lower labeling effort by learning a new class from only one
annotated image and utilizing coarse labels instead, respectively. Previous
works usually fail to leverage the anatomical structure and suffer from class
imbalance and low contrast problems. Hence, we present an innovative framework
for 3D medical image segmentation with one-shot and weakly-supervised settings.
Firstly a propagation-reconstruction network is proposed to project scribbles
from annotated volume to unlabeled 3D images based on the assumption that
anatomical patterns in different human bodies are similar. Then a dual-level
feature denoising module is designed to refine the scribbles based on
anatomical- and pixel-level features. After expanding the scribbles to pseudo
masks, we could train a segmentation model for the new class with the noisy
label training strategy. Experiments on one abdomen and one head-and-neck CT
dataset show the proposed method obtains significant improvement over the
state-of-the-art methods and performs robustly even under severe class
imbalance and low contrast.
