  We propose adversarial constrained-CNN loss, a new paradigm of
constrained-CNN loss methods, for weakly supervised medical image segmentation.
In the new paradigm, prior knowledge is encoded and depicted by reference
masks, and is further employed to impose constraints on segmentation outputs
through adversarial learning with reference masks. Unlike pseudo label methods
for weakly supervised segmentation, such reference masks are used to train a
discriminator rather than a segmentation network, and thus are not required to
be paired with specific images. Our new paradigm not only greatly facilitates
imposing prior knowledge on network's outputs, but also provides stronger and
higher-order constraints, i.e., distribution approximation, through adversarial
learning. Extensive experiments involving different medical modalities,
different anatomical structures, different topologies of the object of
interest, different levels of prior knowledge and weakly supervised annotations
with different annotation ratios is conducted to evaluate our ACCL method.
Consistently superior segmentation results over the size constrained-CNN loss
method have been achieved, some of which are close to the results of full
supervision, thus fully verifying the effectiveness and generalization of our
method. Specifically, we report an average Dice score of 75.4% with an average
annotation ratio of 0.65%, surpassing the prior art, i.e., the size
constrained-CNN loss method, by a large margin of 11.4%. Our codes are made
publicly available at https://github.com/PengyiZhang/ACCL.
