Breast mass segmentation based on ultrasonic entropy maps and attention\n gated U-Net Article Swipe
Michał Byra
,
Piotr Jarosik
,
Katarzyna Dobruch‐Sobczak
,
Ziemowit Klimonda
,
Hanna Piotrzkowska‐Wróblewska
,
Jerzy Litniewski
,
Andrzej Nowicki
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2001.10061
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2001.10061
We propose a novel deep learning based approach to breast mass segmentation\nin ultrasound (US) imaging. In comparison to commonly applied segmentation\nmethods, which use US images, our approach is based on quantitative entropy\nparametric maps. To segment the breast masses we utilized an attention gated\nU-Net convolutional neural network. US images and entropy maps were generated\nbased on raw US signals collected from 269 breast masses. The segmentation\nnetworks were developed separately using US image and entropy maps, and\nevaluated on a test set of 81 breast masses. The attention U-Net trained based\non entropy maps achieved average Dice score of 0.60 (median 0.71), while for\nthe model trained using US images we obtained average Dice score of 0.53\n(median 0.59). Our work presents the feasibility of using quantitative US\nparametric maps for the breast mass segmentation. The obtained results suggest\nthat US parametric maps, which provide the information about local tissue\nscattering properties, might be more suitable for the development of breast\nmass segmentation methods than regular US images.\n
Related Topics
Concepts
Segmentation
Artificial intelligence
Pattern recognition (psychology)
Parametric statistics
Computer science
Entropy (arrow of time)
Breast ultrasound
Convolutional neural network
Deep learning
Image segmentation
Artificial neural network
Mathematics
Breast cancer
Mammography
Statistics
Medicine
Physics
Cancer
Internal medicine
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2001.10061
- https://arxiv.org/pdf/2001.10061
- OA Status
- green
- Cited By
- 7
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287900603
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4287900603Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2001.10061Digital Object Identifier
- Title
-
Breast mass segmentation based on ultrasonic entropy maps and attention\n gated U-NetWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-27Full publication date if available
- Authors
-
Michał Byra, Piotr Jarosik, Katarzyna Dobruch‐Sobczak, Ziemowit Klimonda, Hanna Piotrzkowska‐Wróblewska, Jerzy Litniewski, Andrzej NowickiList of authors in order
- Landing page
-
https://arxiv.org/abs/2001.10061Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2001.10061Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2001.10061Direct OA link when available
- Concepts
-
Segmentation, Artificial intelligence, Pattern recognition (psychology), Parametric statistics, Computer science, Entropy (arrow of time), Breast ultrasound, Convolutional neural network, Deep learning, Image segmentation, Artificial neural network, Mathematics, Breast cancer, Mammography, Statistics, Medicine, Physics, Cancer, Internal medicine, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 3, 2023: 1, 2021: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.maps. | 32 |
| abstract_inverted_index.might | 142 |
| abstract_inverted_index.model | 99 |
| abstract_inverted_index.novel | 3 |
| abstract_inverted_index.score | 92, 108 |
| abstract_inverted_index.using | 67, 101, 118 |
| abstract_inverted_index.which | 21, 134 |
| abstract_inverted_index.while | 97 |
| abstract_inverted_index.0.59). | 111 |
| abstract_inverted_index.0.71), | 96 |
| abstract_inverted_index.breast | 9, 36, 60, 80, 124 |
| abstract_inverted_index.images | 47, 103 |
| abstract_inverted_index.masses | 37 |
| abstract_inverted_index.neural | 44 |
| abstract_inverted_index.(median | 95 |
| abstract_inverted_index.applied | 19 |
| abstract_inverted_index.average | 90, 106 |
| abstract_inverted_index.entropy | 49, 71, 87 |
| abstract_inverted_index.images, | 24 |
| abstract_inverted_index.masses. | 61, 81 |
| abstract_inverted_index.methods | 152 |
| abstract_inverted_index.propose | 1 |
| abstract_inverted_index.provide | 135 |
| abstract_inverted_index.regular | 154 |
| abstract_inverted_index.results | 129 |
| abstract_inverted_index.segment | 34 |
| abstract_inverted_index.signals | 56 |
| abstract_inverted_index.trained | 85, 100 |
| abstract_inverted_index.achieved | 89 |
| abstract_inverted_index.approach | 7, 26 |
| abstract_inverted_index.commonly | 18 |
| abstract_inverted_index.for\nthe | 98 |
| abstract_inverted_index.imaging. | 14 |
| abstract_inverted_index.learning | 5 |
| abstract_inverted_index.network. | 45 |
| abstract_inverted_index.obtained | 105, 128 |
| abstract_inverted_index.presents | 114 |
| abstract_inverted_index.suitable | 145 |
| abstract_inverted_index.utilized | 39 |
| abstract_inverted_index.attention | 41, 83 |
| abstract_inverted_index.based\non | 86 |
| abstract_inverted_index.collected | 57 |
| abstract_inverted_index.developed | 65 |
| abstract_inverted_index.images.\n | 156 |
| abstract_inverted_index.comparison | 16 |
| abstract_inverted_index.parametric | 132 |
| abstract_inverted_index.separately | 66 |
| abstract_inverted_index.ultrasound | 12 |
| abstract_inverted_index.development | 148 |
| abstract_inverted_index.feasibility | 116 |
| abstract_inverted_index.information | 137 |
| abstract_inverted_index.properties, | 141 |
| abstract_inverted_index.breast\nmass | 150 |
| abstract_inverted_index.gated\nU-Net | 42 |
| abstract_inverted_index.quantitative | 30, 119 |
| abstract_inverted_index.segmentation | 151 |
| abstract_inverted_index.0.53\n(median | 110 |
| abstract_inverted_index.convolutional | 43 |
| abstract_inverted_index.segmentation. | 126 |
| abstract_inverted_index.suggest\nthat | 130 |
| abstract_inverted_index.US\nparametric | 120 |
| abstract_inverted_index.and\nevaluated | 73 |
| abstract_inverted_index.generated\nbased | 52 |
| abstract_inverted_index.segmentation\nin | 11 |
| abstract_inverted_index.tissue\nscattering | 140 |
| abstract_inverted_index.entropy\nparametric | 31 |
| abstract_inverted_index.segmentation\nmethods, | 20 |
| abstract_inverted_index.segmentation\nnetworks | 63 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.6739565 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |