Abstract
Automated pathology segmentation remains a valuable diagnostic tool in clinical practice. However, collecting training data is challenging. Semi-supervised approaches by combining labelled and unlabelled data can offer a solution to data scarcity. An approach to semi-supervised learning relies on reconstruction objectives (as self-supervision objectives) that learns in a joint fashion suitable representations for the task. Here, we propose Anatomy-Pathology Disentanglement Network (APD-Net), a pathology segmentation model that attempts to learn jointly for the first time: disentanglement of anatomy, modality, and pathology. The model is trained in a semi-supervised fashion with new reconstruction losses directly aiming to improve pathology segmentation with limited annotations. In addition, a joint optimization strategy is proposed to fully take advantage of the available annotations. We evaluate our methods with two private cardiac infarction segmentation datasets with LGE-MRI scans. APD-Net can perform pathology segmentation with few annotations, maintain performance with different amounts of supervision, and outperform related deep learning methods.
H. Jiang — Most of the work was completed in the School of Engineering at the University of Edinburgh
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Notes
- 1.
We only consider unlabeled pathology, assuming anatomy masks are available during training. Partial anatomy annotation is out of the scope of this paper.
- 2.
Note that \(p^i\) is the same as \(\hat{y}^i_{pat}\), i.e. the predicted pathology mask. We use \(p^i\) for disentanglement and image reconstruction, and \(\hat{y}^i_{pat}\) for pathology segmentation.
- 3.
The Dice loss can be seen as a special case of the Tversky loss [15] when \(\beta =0.5\).
- 4.
The \(\mathbb {1}\) matrix is added to ensure that no zero elements are multiplied with \(\Vert x^i-\hat{x}^i\Vert _1\). Also, if \(\lambda _{pat}=1\), the loss reduces to the \(\ell _1\) loss.
- 5.
U-Net (masked / unmasked) and Cascaded U-Net are optimized with full supervision using Tversky and focal losses, and penalized as defined in the Training details. In reality, U-Net (masked) is not a good choice since manual myocardial annotations are not always available at inference time.
- 6.
Code will be available at https://github.com/falconjhc/APD-Net shortly.
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Acknowledgement
This work was supported by US National Institutes of Health (1R01HL136578-01). This work used resources provided by the Edinburgh Compute and Data Facility (http://www.ecdf.ed.ac.uk/). S.A. Tsaftaris acknowledges the Royal Academy of Engineering and the Research Chairs and Senior Research Fellowships scheme.
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Jiang, H. et al. (2020). Semi-supervised Pathology Segmentation with Disentangled Representations. In: Albarqouni, S., et al. Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning. DART DCL 2020 2020. Lecture Notes in Computer Science(), vol 12444. Springer, Cham. https://doi.org/10.1007/978-3-030-60548-3_7
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