Heart Segmentation From MRI Scans Using Convolutional Neural Network Article Swipe
Shakeel Muhammad Ibrahim
,
Muhammad Sohail Ibrahim
,
Muhammad Usman
,
Imran Naseem
,
Muhammad Moinuddin
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1911.09332
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1911.09332
Heart is one of the vital organs of human body. A minor dysfunction of heart even for a short time interval can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the recent past, various computer assisted medical imaging systems have been proposed for the segmentation of the organ of interest. However, for the segmentation of heart using MRI, only few methods have been proposed each with its own merits and demerits. For further advancement in this area of research, we analyze automated heart segmentation methods for magnetic resonance images. The analysis are based on deep learning methods that processes a full MR scan in a slice by slice fashion to predict desired mask for heart region. We design two encoder decoder type fully convolutional neural network models
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1911.09332
- https://arxiv.org/pdf/1911.09332
- OA Status
- green
- Cited By
- 3
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2990162754
All OpenAlex metadata
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https://openalex.org/W2990162754Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1911.09332Digital Object Identifier
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Heart Segmentation From MRI Scans Using Convolutional Neural NetworkWork title
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preprintOpenAlex work type
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enPrimary language
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2019Year of publication
- Publication date
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2019-11-21Full publication date if available
- Authors
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Shakeel Muhammad Ibrahim, Muhammad Sohail Ibrahim, Muhammad Usman, Imran Naseem, Muhammad MoinuddinList of authors in order
- Landing page
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https://arxiv.org/abs/1911.09332Publisher landing page
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https://arxiv.org/pdf/1911.09332Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1911.09332Direct OA link when available
- Concepts
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Segmentation, Convolutional neural network, Computer science, Artificial intelligence, Magnetic resonance imaging, Encoder, Human heart, Deep learning, Pattern recognition (psychology), Computer vision, Artificial neural network, Medicine, Radiology, Cardiology, Operating systemTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2022: 1, 2021: 1, 2020: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.mask | 122 |
| abstract_inverted_index.only | 68 |
| abstract_inverted_index.scan | 112 |
| abstract_inverted_index.that | 107 |
| abstract_inverted_index.this | 85 |
| abstract_inverted_index.time | 19 |
| abstract_inverted_index.type | 131 |
| abstract_inverted_index.with | 36, 75 |
| abstract_inverted_index.Heart | 0 |
| abstract_inverted_index.based | 102 |
| abstract_inverted_index.body. | 9 |
| abstract_inverted_index.fully | 132 |
| abstract_inverted_index.heart | 14, 65, 92, 124 |
| abstract_inverted_index.human | 8 |
| abstract_inverted_index.minor | 11 |
| abstract_inverted_index.organ | 57 |
| abstract_inverted_index.past, | 42 |
| abstract_inverted_index.short | 18 |
| abstract_inverted_index.slice | 115, 117 |
| abstract_inverted_index.state | 30 |
| abstract_inverted_index.using | 66 |
| abstract_inverted_index.vital | 5 |
| abstract_inverted_index.design | 127 |
| abstract_inverted_index.fatal, | 23 |
| abstract_inverted_index.merits | 78 |
| abstract_inverted_index.models | 136 |
| abstract_inverted_index.neural | 134 |
| abstract_inverted_index.organs | 6 |
| abstract_inverted_index.recent | 41 |
| abstract_inverted_index.analyze | 90 |
| abstract_inverted_index.decoder | 130 |
| abstract_inverted_index.desired | 121 |
| abstract_inverted_index.encoder | 129 |
| abstract_inverted_index.fashion | 118 |
| abstract_inverted_index.further | 82 |
| abstract_inverted_index.images. | 98 |
| abstract_inverted_index.imaging | 47 |
| abstract_inverted_index.medical | 46 |
| abstract_inverted_index.methods | 70, 94, 106 |
| abstract_inverted_index.network | 135 |
| abstract_inverted_index.predict | 120 |
| abstract_inverted_index.region. | 125 |
| abstract_inverted_index.systems | 48 |
| abstract_inverted_index.various | 43 |
| abstract_inverted_index.However, | 60 |
| abstract_inverted_index.analysis | 100 |
| abstract_inverted_index.assisted | 45 |
| abstract_inverted_index.computer | 44 |
| abstract_inverted_index.interval | 20 |
| abstract_inverted_index.learning | 105 |
| abstract_inverted_index.magnetic | 96 |
| abstract_inverted_index.patients | 35 |
| abstract_inverted_index.proposed | 51, 73 |
| abstract_inverted_index.automated | 91 |
| abstract_inverted_index.demerits. | 80 |
| abstract_inverted_index.diseases. | 38 |
| abstract_inverted_index.efficient | 25 |
| abstract_inverted_index.essential | 32 |
| abstract_inverted_index.interest. | 59 |
| abstract_inverted_index.processes | 108 |
| abstract_inverted_index.research, | 88 |
| abstract_inverted_index.resonance | 97 |
| abstract_inverted_index.monitoring | 26 |
| abstract_inverted_index.therefore, | 24 |
| abstract_inverted_index.advancement | 83 |
| abstract_inverted_index.dysfunction | 12 |
| abstract_inverted_index.segmentation | 54, 63, 93 |
| abstract_inverted_index.convolutional | 133 |
| abstract_inverted_index.physiological | 29 |
| abstract_inverted_index.cardiovascular | 37 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile |