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Semi-supervised Instance Segmentation with a Learned Shape Prior

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12446)


To date, most instance segmentation approaches are based on supervised learning that requires a considerable amount of annotated object contours as training ground truth. Here, we propose a framework that searches for the target object based on a shape prior. The shape prior model is learned with a variational autoencoder that requires only a very limited amount of training data: In our experiments, a few dozens of object shape patches from the target dataset, as well as purely synthetic shapes, were sufficient to achieve results en par with supervised methods with full access to training data on two out of three cell segmentation datasets. Our method with a synthetic shape prior was superior to pre-trained supervised models with access to limited domain-specific training data on all three datasets. Since the learning of prior models requires shape patches, whether real or synthetic data, we call this framework semi-supervised learning. The code is available to the public (


  • Semi-supervised
  • Instance segmentation
  • Shape prior
  • Variational autoencoder
  • Edge loss

This work was supported by the Deutsche Forschungsgemeinschaft (Research Training Group 2416 MultiSenses-MultiScales).

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Correspondence to Long Chen .

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Chen, L., Zhang, W., Wu, Y., Strauch, M., Merhof, D. (2020). Semi-supervised Instance Segmentation with a Learned Shape Prior. In: Cardoso, J., et al. Interpretable and Annotation-Efficient Learning for Medical Image Computing. IMIMIC MIL3ID LABELS 2020 2020 2020. Lecture Notes in Computer Science(), vol 12446. Springer, Cham.

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  • Print ISBN: 978-3-030-61165-1

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