Large-scale multi-center CT and MRI segmentation of pancreas with deep learning Article Swipe
Zheyuan Zhang
,
Elif Keleş
,
Görkem Durak
,
Yavuz Taktak
,
Onkar Susladkar
,
Vandan Gorade
,
Debesh Jha
,
Asli C. Ormeci
,
Alpay Medetalibeyoğlu
,
Lanhong Yao
,
Bin Wang
,
Ilkin Isler
,
Linkai Peng
,
Hongyi Pan
,
Camila Lopes Vendrami
,
Amir Bourhani
,
Yury Velichko
,
Boqing Gong
,
Concetto Spampinato
,
Ayis Pyrros
,
Pallavi Tiwari
,
Derk C.F. Klatte
,
Megan Engels
,
Sanne Hoogenboom
,
Candice W. Bolan
,
Emil Agarunov
,
Nassier Harfouch
,
Chenchan Huang
,
Marco J. Bruno
,
Ivo G. Schoots
,
Rajesh N. Keswani
,
Frank H. Miller
,
Tamas A. Gonda
,
Cemal Yazıcı
,
Temel Tirkes
,
Barış Türkbey
,
Michael B. Wallace
,
Ulaş Bağcı
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.media.2024.103382
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.media.2024.103382
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1 W) and T2-weighted (T2 W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We introduced a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (±7.2%, at case level) with CT, 85.0% (±7.9%) with T1 W MRI, and 86.3% (±6.4%) with T2 W MRI. There was a high correlation for pancreas volume prediction with R2 of 0.91, 0.84, and 0.85 for CT, T1 W, and T2 W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1 W and T2 W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/. Our source code is available at https://github.com/NUBagciLab/PaNSegNet.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.media.2024.103382
- OA Status
- hybrid
- Cited By
- 33
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404171396
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404171396Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.media.2024.103382Digital Object Identifier
- Title
-
Large-scale multi-center CT and MRI segmentation of pancreas with deep learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-08Full publication date if available
- Authors
-
Zheyuan Zhang, Elif Keleş, Görkem Durak, Yavuz Taktak, Onkar Susladkar, Vandan Gorade, Debesh Jha, Asli C. Ormeci, Alpay Medetalibeyoğlu, Lanhong Yao, Bin Wang, Ilkin Isler, Linkai Peng, Hongyi Pan, Camila Lopes Vendrami, Amir Bourhani, Yury Velichko, Boqing Gong, Concetto Spampinato, Ayis Pyrros, Pallavi Tiwari, Derk C.F. Klatte, Megan Engels, Sanne Hoogenboom, Candice W. Bolan, Emil Agarunov, Nassier Harfouch, Chenchan Huang, Marco J. Bruno, Ivo G. Schoots, Rajesh N. Keswani, Frank H. Miller, Tamas A. Gonda, Cemal Yazıcı, Temel Tirkes, Barış Türkbey, Michael B. Wallace, Ulaş BağcıList of authors in order
- Landing page
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https://doi.org/10.1016/j.media.2024.103382Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.media.2024.103382Direct OA link when available
- Concepts
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Artificial intelligence, Deep learning, Segmentation, Scale (ratio), Computer vision, Center (category theory), Computer science, Pattern recognition (psychology), Radiology, Medicine, Cartography, Geography, Crystallography, ChemistryTop concepts (fields/topics) attached by OpenAlex
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33Total citation count in OpenAlex
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2025: 30, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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67Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.0.91, | 205 |
| abstract_inverted_index.1,350 | 87 |
| abstract_inverted_index.2,117 | 132 |
| abstract_inverted_index.2022. | 80 |
| abstract_inverted_index.85.0% | 180 |
| abstract_inverted_index.86.3% | 187 |
| abstract_inverted_index.88.3% | 173 |
| abstract_inverted_index.March | 76 |
| abstract_inverted_index.There | 193 |
| abstract_inverted_index.While | 18 |
| abstract_inverted_index.found | 218 |
| abstract_inverted_index.intra | 151 |
| abstract_inverted_index.kappa | 148 |
| abstract_inverted_index.large | 54 |
| abstract_inverted_index.scans | 57, 85 |
| abstract_inverted_index.total | 130 |
| abstract_inverted_index.(0.624 | 221 |
| abstract_inverted_index.(HD95) | 142 |
| abstract_inverted_index.called | 103 |
| abstract_inverted_index.level) | 177 |
| abstract_inverted_index.linear | 117 |
| abstract_inverted_index.module | 119 |
| abstract_inverted_index.needed | 10 |
| abstract_inverted_index.nnUNet | 109 |
| abstract_inverted_index.paired | 157 |
| abstract_inverted_index.scans) | 133 |
| abstract_inverted_index.series | 71 |
| abstract_inverted_index.source | 246 |
| abstract_inverted_index.study, | 50 |
| abstract_inverted_index.tested | 124 |
| abstract_inverted_index.volume | 160, 200 |
| abstract_inverted_index.Cohen's | 147 |
| abstract_inverted_index.between | 75 |
| abstract_inverted_index.centers | 74 |
| abstract_inverted_index.dataset | 55 |
| abstract_inverted_index.imaging | 8 |
| abstract_inverted_index.largely | 30 |
| abstract_inverted_index.method, | 102 |
| abstract_inverted_index.methods | 27 |
| abstract_inverted_index.network | 113 |
| abstract_inverted_index.scores. | 236 |
| abstract_inverted_index.sources | 92 |
| abstract_inverted_index.t-tests | 158 |
| abstract_inverted_index.(±6.4%) | 188 |
| abstract_inverted_index.(±7.2%, | 174 |
| abstract_inverted_index.(±7.9%) | 181 |
| abstract_inverted_index.CT-based | 19 |
| abstract_inverted_index.November | 79 |
| abstract_inverted_index.accuracy | 126 |
| abstract_inverted_index.achieved | 169 |
| abstract_inverted_index.distance | 141 |
| abstract_inverted_index.efforts, | 41 |
| abstract_inverted_index.enabling | 120 |
| abstract_inverted_index.learning | 45 |
| abstract_inverted_index.methods. | 46 |
| abstract_inverted_index.metrics. | 144 |
| abstract_inverted_index.moderate | 219 |
| abstract_inverted_index.pancreas | 5, 100, 199 |
| abstract_inverted_index.patients | 88 |
| abstract_inverted_index.publicly | 36, 90 |
| abstract_inverted_index.research | 40 |
| abstract_inverted_index.settings | 136 |
| abstract_inverted_index.Automated | 0 |
| abstract_inverted_index.Hausdorff | 140 |
| abstract_inverted_index.MRI-based | 25 |
| abstract_inverted_index.abdominal | 69 |
| abstract_inverted_index.accuracy, | 167 |
| abstract_inverted_index.agreement | 154, 235 |
| abstract_inverted_index.attention | 118 |
| abstract_inverted_index.available | 37, 91, 242, 249 |
| abstract_inverted_index.collected | 52, 83 |
| abstract_inverted_index.combining | 105 |
| abstract_inverted_index.datasets, | 38 |
| abstract_inverted_index.diagnosis | 12 |
| abstract_inverted_index.diseases. | 17 |
| abstract_inverted_index.follow-up | 14 |
| abstract_inverted_index.purposes. | 95 |
| abstract_inverted_index.strengths | 107 |
| abstract_inverted_index.PanSegNet, | 104 |
| abstract_inverted_index.evaluation | 143, 155 |
| abstract_inverted_index.introduced | 97 |
| abstract_inverted_index.pancreatic | 16, 20 |
| abstract_inverted_index.prediction | 201 |
| abstract_inverted_index.statistics | 149 |
| abstract_inverted_index.volumetric | 1, 121 |
| abstract_inverted_index.PanSegNet's | 125 |
| abstract_inverted_index.T1-weighted | 62 |
| abstract_inverted_index.T2-weighted | 66 |
| abstract_inverted_index.Transformer | 112 |
| abstract_inverted_index.correlation | 197 |
| abstract_inverted_index.inter-rater | 153 |
| abstract_inverted_index.benchmarking | 39, 94 |
| abstract_inverted_index.coefficients | 171 |
| abstract_inverted_index.comparisons, | 163 |
| abstract_inverted_index.computation. | 122 |
| abstract_inverted_index.cross-center | 135 |
| abstract_inverted_index.established, | 24 |
| abstract_inverted_index.segmentation | 2, 21, 26, 101, 166 |
| abstract_inverted_index.R<sup>2</sup> | 203 |
| abstract_inverted_index.participants) | 60 |
| abstract_inverted_index.respectively) | 231 |
| abstract_inverted_index.respectively. | 164, 216 |
| abstract_inverted_index.retrospective | 49 |
| abstract_inverted_index.understudied, | 29 |
| abstract_inverted_index.cross-modality | 128 |
| abstract_inverted_index.inter-observer | 220 |
| abstract_inverted_index.intra-observer | 234 |
| abstract_inverted_index.cross-sectional | 7 |
| abstract_inverted_index.domain-specific | 43 |
| abstract_inverted_index.https://osf.io/kysnj/. | 244 |
| abstract_inverted_index.https://github.com/NUBagciLab/PaNSegNet. | 251 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 38 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.99067239 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |