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% \jmlrvolume{-- Under Review}
\jmlryear{2021}
\jmlrworkshop{Full Paper -- MIDL 2021}
% \editors{Under Review for MIDL 2021}

\title[Weakly Supervised Segmentation via Self-taught Model]{Weakly Supervised Volumetric Segmentation via Self-taught \\Shape Denoising Model}

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%\footnotetext[1]{Contributed equally}

% More complicate cases, e.g. with dual affiliations and joint authorship
\midlauthor{\Name{Qian He\midljointauthortext{Contributed equally}\nametag{$^{1,2,3}$}} \Email{heqian@shanghaitech.edu.cn}\\
\Name{Shuailin Li\midlotherjointauthor\nametag{$^{1}$}} \Email{lishl@shanghaitech.edu.cn}\\
\Name{Xuming He\nametag{$^{1,4}$}} \Email{hexm@shanghaitech.edu.cn}\\
\addr $^{1}$ School of Information Science and Technology, ShanghaiTech University\\
\addr $^{2}$ Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences\\
\addr $^{3}$ University of Chinese Academy of Sciences\\
\addr $^{4}$ Shanghai Engineering Research Center of Intelligent Vision and Imaging
}

\begin{document}

\maketitle
%\vspace{-1cm}

%%%%%%%%% ABSTRACT
\begin{abstract}
    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. Our code is available at: \href{https://github.com/Seolen/weak_seg_via_shape_model}{https://github.com/Seolen/weak\_seg\_via\_shape\_model}.
    
    %Existing weak label methods for 2D natural images are often inefficient for volumetric data due to the lack of modeling 3D structure. 
    %One main defect of those 2D methods is that they explore little image continuity and correlation in 3D volumetric space, and thus leads to label redundancy. 
    
%    To address this, we design a novel framework, including a Semantic Segmentation Network (SSN) and a Shape Denoising Network (SDN). 
%    Our main idea is to extract a self-taught shape prior leveraging weak labels, and then to integrate this prior into segmentation prediction with our SDN for shape denoising and refinement. 
%    To train our model, we develop an iterative learning strategy to incrementally incorporate learned shape prior and to update our model. 
%    Moreover, we develop a weak annotation strategy specifically for volumetric image segmentation, including slice selection and a hybrid label design. At the same labeling cost, our annotation is more informative for training segmentation models. 
%    The empirical experiments show that our approach outperforms existing SOTA strategies on three organ segmentation benchmarks with different shape variations. Remarkably, we can achieve strong performance with even 10\% labeled slices, which is significantly superior to other methods.
\end{abstract}

\begin{keywords}
Weakly supervised segmentation, 3D shape prior, self-taught learning. %shape denoising, 
\end{keywords}

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% % Acknowledgments---Will not appear in anonymized version
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\bibliography{he21}


\appendix
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\end{document}
