Segmentation Certainty Through Uncertainty: Uncertainty-Refined Binary Volumetric Segmentation Under Multifactor Domain Shift
Open accessAbstract
Deep learning segmentation models are known to be sensitive to the scale, contrast, and distribution of pixel values when applied to Computed Tomography (CT) images. For material samples, scans are often obtained from a variety of scanning equipment and resolutions resulting in domain shift. The ability of segmentation models to generalize to examples from these shifted domains relies on how well the distribution of the training data represents the overall distribution of the target data. We present a method to overcome the challenges presented by domain shifts. Our results indicate that we can leverage a deep learning model trained on one domain to accurately segment similar materials at different resolutions by refining binary predictions using uncertainty quantification (UQ). We apply this technique to a set of unlabeled CT scans of woven composite materials with clear qualitative improvement of binary segmentations over the original deep learning predictions. In contrast to prior work, our technique enables refined segmentations without the expense of the additional training time and parameters associated with deep learning models used to address domain shift.
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Bayesian-based GNNs/GCNs and Deep graph BO have shown promising outcomes for quantifying uncertainties in GNNs/GCNs.
“Our findings reveal that even though Bayesian-based GNNs/GCNs (Graph Convolutional Networks) [280], [593], [594], [595] and Deep graph BO [596] have shown promising outcomes for quantifying uncertainties in GNNs/GCNs, but there are a limited number of studies in this research domain.”
From A review of uncertainty quantification in deep learning: Techniques, applications and challenges (2021) · cites this paper as [280]
The Method and Experiment sections describe quantifying uncertainty with dropout in a 3D convolutional neural network for CT-image segmentation. They do not study GNNs or GCNs, so the source does not support the claim that those graph models have shown promising outcomes for uncertainty quantification. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThere are a limited number of studies on quantifying uncertainties in GNNs/GCNs.
“Our findings reveal that even though Bayesian-based GNNs/GCNs (Graph Convolutional Networks) [280], [593], [594], [595] and Deep graph BO [596] have shown promising outcomes for quantifying uncertainties in GNNs/GCNs, but there are a limited number of studies in this research domain.”
From A review of uncertainty quantification in deep learning: Techniques, applications and challenges (2021) · cites this paper as [280]
The Method and Experiment sections describe uncertainty quantification for a 3D CNN that segments CT scans, not for GNNs or GCNs. The Related Work section does not assess how many studies quantify uncertainty in graph neural networks (searched: GNNs/GCNs, graph convolutional networks, and Bayesian uncertainty). (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Cites
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 2,720 · see citing works
“To date, there have been number of studies addressed uncertainty in deep learning algorithms for various applications including but not limited to image/video retrieval [399], [400], depth estimation [401], [402], object detection [403], [404], [405], semantic segmentation and scene understanding [406], [407], [408],…”
Background
- Authors
- Martinez, C., Potter, K. M., Smith, M. D., Donahue, E. A., Collins, L., Korbin, J. P., & Roberts, S. A.
- Affiliations
- Sandia National Laboratories (US)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 484-486
- OpenAlex
- W2971828241
- Cited by
- 10
- References
- 12
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 3 · 2022: 1 · 2021: 2 · 2020: 1
- FWCI
- 0.33
- Type
- conference paper
- Language
- en
- Open access
- green
- www.osti.gov
- Licence
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- 3D image segmentation, uncertainty quantification, domain shift, binary segmentation, computed tomography, material microstructure, segmentation refinement
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