Semi-supervised Instance Segmentation with a Learned Shape Prior
Long Chen Weiwen Zhang Yuli Wu Martin Strauch Dorit Merhof
To date, most instance segmentation approaches are based on supervised
learning that requires a considerable amount of annotated object contours as
training ground truth. Here, we propose a framework that searches for the
target object based on a shape prior. The shape prior model is learned with a
variational autoencoder that requires only a very limited amount of training
data: In our experiments, a few dozens of object shape patches from the target
dataset, as well as purely synthetic shapes, were sufficient to achieve results
en par with supervised methods with full access to training data on two out of
three cell segmentation datasets. Our method with a synthetic shape prior was
superior to pre-trained supervised models with access to limited
domain-specific training data on all three datasets. Since the learning of
prior models requires shape patches, whether real or synthetic data, we call
this framework semi-supervised learning.