  Weakly supervised segmentation is an important problem in medical image
analysis due to the high cost of pixelwise annotation. Prior methods, while
often focusing on weak labels of 2D images, exploit few structural cues of
volumetric medical images. To address this, we propose a novel
weakly-supervised segmentation strategy capable of better capturing 3D shape
prior in both model prediction and learning. Our main idea is to extract a
self-taught shape representation by leveraging weak labels, and then integrate
this representation into segmentation prediction for shape refinement. To this
end, we design a deep network consisting of a segmentation module and a shape
denoising module, which are trained by an iterative learning strategy.
Moreover, we introduce a weak annotation scheme with a hybrid label design for
volumetric images, which improves model learning without increasing the overall
annotation cost. The empirical experiments show that our approach outperforms
existing SOTA strategies on three organ segmentation benchmarks with
distinctive shape properties. Notably, we can achieve strong performance with
even 10\% labeled slices, which is significantly superior to other methods.
