Incorporating Boundary Uncertainty into loss functions for biomedical
image segmentation
- URL: http://arxiv.org/abs/2111.00533v1
- Date: Sun, 31 Oct 2021 16:19:57 GMT
- Title: Incorporating Boundary Uncertainty into loss functions for biomedical
image segmentation
- Authors: Michael Yeung, Guang Yang, Evis Sala, Carola-Bibiane Sch\"onlieb,
Leonardo Rundo
- Abstract summary: We propose the Boundary Uncertainty, which uses morphological operations to restrict soft labelling to object boundaries.
We incorporate Boundary Uncertainty with the Dice loss, achieving consistently improved performance across three well-validated biomedical imaging datasets.
- Score: 2.5243042477020836
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Manual segmentation is used as the gold-standard for evaluating neural
networks on automated image segmentation tasks. Due to considerable
heterogeneity in shapes, colours and textures, demarcating object boundaries is
particularly difficult in biomedical images, resulting in significant inter and
intra-rater variability. Approaches, such as soft labelling and distance
penalty term, apply a global transformation to the ground truth, redefining the
loss function with respect to uncertainty. However, global operations are
computationally expensive, and neither approach accurately reflects the
uncertainty underlying manual annotation. In this paper, we propose the
Boundary Uncertainty, which uses morphological operations to restrict soft
labelling to object boundaries, providing an appropriate representation of
uncertainty in ground truth labels, and may be adapted to enable robust model
training where systematic manual segmentation errors are present. We
incorporate Boundary Uncertainty with the Dice loss, achieving consistently
improved performance across three well-validated biomedical imaging datasets
compared to soft labelling and distance-weighted penalty. Boundary Uncertainty
not only more accurately reflects the segmentation process, but it is also
efficient, robust to segmentation errors and exhibits better generalisation.
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