Unsupervised Region-Based Anomaly Detection In Brain MRI With Adversarial Image Inpainting Article Swipe
Bao T. Nguyen
,
Arthur M. Feldman
,
Sarath Bethapudi
,
Andrew Jennings
,
Chris G. Willcocks
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/isbi48211.2021.9434115
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/isbi48211.2021.9434115
Medical segmentation is performed to determine the bounds of regions of interest (ROI) prior to surgery. By allowing the study of growth, structure, and behaviour of the ROI in the planning phase, critical information can be obtained, increasing the likelihood of a successful operation. Usually, segmentations are performed manually or via machine learning methods trained on manual annotations. In contrast, this paper proposes a fully automatic, unsupervised inpainting-based brain tumour segmentation system for T1-weighted MRI. First, a deep convolutional neural network (DCNN) is trained to reconstruct missing healthy brain regions. Then, upon application, anomalous regions are determined by identifying areas of highest reconstruction loss. Finally, superpixel segmentation is performed to segment those regions. We show the proposed system is able to segment various sized and abstract tumours and achieves a mean and standard deviation Dice score of 0.771 and 0.176, respectively.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/isbi48211.2021.9434115
- OA Status
- gold
- Cited By
- 34
- References
- 27
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3165355476Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/isbi48211.2021.9434115Digital Object Identifier
- Title
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Unsupervised Region-Based Anomaly Detection In Brain MRI With Adversarial Image InpaintingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
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2021-04-13Full publication date if available
- Authors
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Bao T. Nguyen, Arthur M. Feldman, Sarath Bethapudi, Andrew Jennings, Chris G. WillcocksList of authors in order
- Landing page
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https://doi.org/10.1109/isbi48211.2021.9434115Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://durham-repository.worktribe.com/output/1141356Direct OA link when available
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Artificial intelligence, Segmentation, Computer science, Inpainting, Pattern recognition (psychology), Convolutional neural network, Region of interest, Image segmentation, Deep learning, Anomaly detection, Computer vision, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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34Total citation count in OpenAlex
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2025: 3, 2024: 7, 2023: 13, 2022: 8, 2021: 3Per-year citation counts (last 5 years)
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.show | 114 |
| abstract_inverted_index.this | 60 |
| abstract_inverted_index.upon | 91 |
| abstract_inverted_index.(ROI) | 12 |
| abstract_inverted_index.0.771 | 137 |
| abstract_inverted_index.Then, | 90 |
| abstract_inverted_index.areas | 99 |
| abstract_inverted_index.brain | 68, 88 |
| abstract_inverted_index.fully | 64 |
| abstract_inverted_index.loss. | 103 |
| abstract_inverted_index.paper | 61 |
| abstract_inverted_index.prior | 13 |
| abstract_inverted_index.score | 135 |
| abstract_inverted_index.sized | 123 |
| abstract_inverted_index.study | 19 |
| abstract_inverted_index.those | 111 |
| abstract_inverted_index.(DCNN) | 81 |
| abstract_inverted_index.0.176, | 139 |
| abstract_inverted_index.First, | 75 |
| abstract_inverted_index.bounds | 7 |
| abstract_inverted_index.manual | 56 |
| abstract_inverted_index.neural | 79 |
| abstract_inverted_index.phase, | 31 |
| abstract_inverted_index.system | 71, 117 |
| abstract_inverted_index.tumour | 69 |
| abstract_inverted_index.Medical | 0 |
| abstract_inverted_index.growth, | 21 |
| abstract_inverted_index.healthy | 87 |
| abstract_inverted_index.highest | 101 |
| abstract_inverted_index.machine | 51 |
| abstract_inverted_index.methods | 53 |
| abstract_inverted_index.missing | 86 |
| abstract_inverted_index.network | 80 |
| abstract_inverted_index.regions | 9, 94 |
| abstract_inverted_index.segment | 110, 121 |
| abstract_inverted_index.trained | 54, 83 |
| abstract_inverted_index.tumours | 126 |
| abstract_inverted_index.various | 122 |
| abstract_inverted_index.Finally, | 104 |
| abstract_inverted_index.Usually, | 44 |
| abstract_inverted_index.abstract | 125 |
| abstract_inverted_index.achieves | 128 |
| abstract_inverted_index.allowing | 17 |
| abstract_inverted_index.critical | 32 |
| abstract_inverted_index.interest | 11 |
| abstract_inverted_index.learning | 52 |
| abstract_inverted_index.manually | 48 |
| abstract_inverted_index.planning | 30 |
| abstract_inverted_index.proposed | 116 |
| abstract_inverted_index.proposes | 62 |
| abstract_inverted_index.regions. | 89, 112 |
| abstract_inverted_index.standard | 132 |
| abstract_inverted_index.surgery. | 15 |
| abstract_inverted_index.anomalous | 93 |
| abstract_inverted_index.behaviour | 24 |
| abstract_inverted_index.contrast, | 59 |
| abstract_inverted_index.determine | 5 |
| abstract_inverted_index.deviation | 133 |
| abstract_inverted_index.obtained, | 36 |
| abstract_inverted_index.performed | 3, 47, 108 |
| abstract_inverted_index.automatic, | 65 |
| abstract_inverted_index.determined | 96 |
| abstract_inverted_index.increasing | 37 |
| abstract_inverted_index.likelihood | 39 |
| abstract_inverted_index.operation. | 43 |
| abstract_inverted_index.structure, | 22 |
| abstract_inverted_index.successful | 42 |
| abstract_inverted_index.superpixel | 105 |
| abstract_inverted_index.T1-weighted | 73 |
| abstract_inverted_index.identifying | 98 |
| abstract_inverted_index.information | 33 |
| abstract_inverted_index.reconstruct | 85 |
| abstract_inverted_index.annotations. | 57 |
| abstract_inverted_index.application, | 92 |
| abstract_inverted_index.segmentation | 1, 70, 106 |
| abstract_inverted_index.unsupervised | 66 |
| abstract_inverted_index.convolutional | 78 |
| abstract_inverted_index.respectively. | 140 |
| abstract_inverted_index.segmentations | 45 |
| abstract_inverted_index.reconstruction | 102 |
| abstract_inverted_index.inpainting-based | 67 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.94979796 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |