\section{Conclusion}
In this work, we have developed a novel approach and proposed a sparse annotation scheme for weakly supervised volumetric segmentation. Our proposed self-taught shape denoising network is able to refine the segmented masks into better shapes and we improve our model performance by iteratively updating the model with learned shape prior. Moreover, our sparse annotation scheme %reduces annotation cost and our hybrid label 
provides more information comparing to other weak labels at the same labeling cost. Evaluation on three benchmarks with distinctive shape properties shows that our method robustly outperforms other state-of-the-art methods utilizing different weak annotations.