  Despite that deep learning has achieved state-of-the-art performance for
medical image segmentation, its success relies on a large set of manually
annotated images for training that are expensive to acquire. In this paper, we
propose an annotation-efficient learning framework for segmentation tasks that
avoids annotations of training images, where we use an improved
Cycle-Consistent Generative Adversarial Network (GAN) to learn from a set of
unpaired medical images and auxiliary masks obtained either from a shape model
or public datasets. We first use the GAN to generate pseudo labels for our
training images under the implicit high-level shape constraint represented by a
Variational Auto-encoder (VAE)-based discriminator with the help of the
auxiliary masks, and build a Discriminator-guided Generator Channel Calibration
(DGCC) module which employs our discriminator's feedback to calibrate the
generator for better pseudo labels. To learn from the pseudo labels that are
noisy, we further introduce a noise-robust iterative learning method using
noise-weighted Dice loss. We validated our framework with two situations:
objects with a simple shape model like optic disc in fundus images and fetal
head in ultrasound images, and complex structures like lung in X-Ray images and
liver in CT images. Experimental results demonstrated that 1) Our VAE-based
discriminator and DGCC module help to obtain high-quality pseudo labels. 2) Our
proposed noise-robust learning method can effectively overcome the effect of
noisy pseudo labels. 3) The segmentation performance of our method without
using annotations of training images is close or even comparable to that of
learning from human annotations.
