Weakly Supervised Volumetric Segmentation via Self-taught Shape Denoising ModelDownload PDF

Feb 09, 2021 (edited Apr 26, 2021)MIDL 2021 Conference SubmissionReaders: Everyone
  • Keywords: Weakly supervised segmentation, 3D shape prior, self-taught learning.
  • 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.
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  • Source Code Url: https://github.com/Seolen/weak_seg_via_shape_model
  • Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
  • Paper Type: methodological development
  • Source Latex: zip
  • Primary Subject Area: Segmentation
  • Secondary Subject Area: Learning with Noisy Labels and Limited Data
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