Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet Article Swipe
Mobarakol Islam
,
Vibashan VS
,
V. Jeya Maria Jose
,
Navodini Wijethilake
,
Utkarsh Uppal
,
Hongliang Ren
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.00985
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.00985
In this work, we develop an attention convolutional neural network (CNN) to segment brain tumors from Magnetic Resonance Images (MRI). Further, we predict the survival rate using various machine learning methods. We adopt a 3D UNet architecture and integrate channel and spatial attention with the decoder network to perform segmentation. For survival prediction, we extract some novel radiomic features based on geometry, location, the shape of the segmented tumor and combine them with clinical information to estimate the survival duration for each patient. We also perform extensive experiments to show the effect of each feature for overall survival (OS) prediction. The experimental results infer that radiomic features such as histogram, location, and shape of the necrosis region and clinical features like age are the most critical parameters to estimate the OS.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2104.00985
- https://arxiv.org/pdf/2104.00985
- OA Status
- green
- Cited By
- 2
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3147881133
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3147881133Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2104.00985Digital Object Identifier
- Title
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Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-04-02Full publication date if available
- Authors
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Mobarakol Islam, Vibashan VS, V. Jeya Maria Jose, Navodini Wijethilake, Utkarsh Uppal, Hongliang RenList of authors in order
- Landing page
-
https://arxiv.org/abs/2104.00985Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2104.00985Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2104.00985Direct OA link when available
- Concepts
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Segmentation, Artificial intelligence, Pattern recognition (psychology), Convolutional neural network, Computer science, Histogram, Feature (linguistics), Magnetic resonance imaging, Survival analysis, Machine learning, Image (mathematics), Mathematics, Medicine, Radiology, Statistics, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 1, 2020: 1Per-year citation counts (last 5 years)
- References (count)
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20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.feature | 94 |
| abstract_inverted_index.machine | 28 |
| abstract_inverted_index.network | 9, 46 |
| abstract_inverted_index.overall | 96 |
| abstract_inverted_index.perform | 48, 85 |
| abstract_inverted_index.predict | 22 |
| abstract_inverted_index.results | 102 |
| abstract_inverted_index.segment | 12 |
| abstract_inverted_index.spatial | 41 |
| abstract_inverted_index.various | 27 |
| abstract_inverted_index.Further, | 20 |
| abstract_inverted_index.Magnetic | 16 |
| abstract_inverted_index.clinical | 73, 118 |
| abstract_inverted_index.critical | 125 |
| abstract_inverted_index.duration | 79 |
| abstract_inverted_index.estimate | 76, 128 |
| abstract_inverted_index.features | 58, 106, 119 |
| abstract_inverted_index.learning | 29 |
| abstract_inverted_index.methods. | 30 |
| abstract_inverted_index.necrosis | 115 |
| abstract_inverted_index.patient. | 82 |
| abstract_inverted_index.radiomic | 57, 105 |
| abstract_inverted_index.survival | 24, 51, 78, 97 |
| abstract_inverted_index.Resonance | 17 |
| abstract_inverted_index.attention | 6, 42 |
| abstract_inverted_index.extensive | 86 |
| abstract_inverted_index.geometry, | 61 |
| abstract_inverted_index.integrate | 38 |
| abstract_inverted_index.location, | 62, 110 |
| abstract_inverted_index.segmented | 67 |
| abstract_inverted_index.histogram, | 109 |
| abstract_inverted_index.parameters | 126 |
| abstract_inverted_index.experiments | 87 |
| abstract_inverted_index.information | 74 |
| abstract_inverted_index.prediction, | 52 |
| abstract_inverted_index.prediction. | 99 |
| abstract_inverted_index.architecture | 36 |
| abstract_inverted_index.experimental | 101 |
| abstract_inverted_index.convolutional | 7 |
| abstract_inverted_index.segmentation. | 49 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |