An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy Article Swipe
Ebbe Laugaard Lorenzen
,
Bahar Çelik
,
Nis Sarup
,
Lars Dysager
,
Rasmus Lübeck Christiansen
,
A. Bertelsen
,
Uffe Bernchou
,
Søren Nielsen Agergaard
,
Maximilian Konrad
,
Carsten Brink
,
Faisal Mahmood
,
Tine Schytte
,
Christina Junker Nyborg
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2023.1285725
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2023.1285725
Background Adaptive MRI-guided radiotherapy (MRIgRT) requires accurate and efficient segmentation of organs and targets on MRI scans. Manual segmentation is time-consuming and variable, while deformable image registration (DIR)-based contour propagation may not account for large anatomical changes. Therefore, we developed and evaluated an automatic segmentation method using the nnU-net framework. Methods The network was trained on 38 patients (76 scans) with localized prostate cancer and tested on 30 patients (60 scans) with localized prostate, metastatic prostate, or bladder cancer treated at a 1.5 T MRI-linac at our institution. The performance of the network was compared with the current clinical workflow based on DIR. The segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD) metrics. Results The trained network successfully segmented all 600 structures in the test set. High similarity was obtained for most structures, with 90% of the contours having a DSC above 0.9 and 86% having an MSD below 1 mm. The largest discrepancies were found in the sigmoid and colon structures. Stratified analysis on cancer type showed that the best performance was seen in the same type of patients that the model was trained on (localized prostate). Especially in patients with bladder cancer, the performance was lower for the bladder and the surrounding organs. A complete automatic delineation workflow took approximately 1 minute. Compared with contour transfer based on the clinically used DIR algorithm, the nnU-net performed statistically better across all organs, with the most significant gain in using the nnU-net seen for organs subject to more considerable volumetric changes due to variation in the filling of the rectum, bladder, bowel, and sigmoid. Conclusion We successfully trained and tested a network for automatically segmenting organs and targets for MRIgRT in the male pelvis region. Good test results were seen for the trained nnU-net, with test results outperforming the current clinical practice using DIR-based contour propagation at the 1.5 T MRI-linac. The trained network is sufficiently fast and accurate for clinical use in an online setting for MRIgRT. The model is provided as open-source.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fonc.2023.1285725
- OA Status
- gold
- Cited By
- 8
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388297959
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388297959Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fonc.2023.1285725Digital Object Identifier
- Title
-
An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-03Full publication date if available
- Authors
-
Ebbe Laugaard Lorenzen, Bahar Çelik, Nis Sarup, Lars Dysager, Rasmus Lübeck Christiansen, A. Bertelsen, Uffe Bernchou, Søren Nielsen Agergaard, Maximilian Konrad, Carsten Brink, Faisal Mahmood, Tine Schytte, Christina Junker NyborgList of authors in order
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https://doi.org/10.3389/fonc.2023.1285725Publisher landing page
- Open access
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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://doi.org/10.3389/fonc.2023.1285725Direct OA link when available
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Segmentation, Open source, Radiation therapy, Computer science, Algorithm, Chemo-radiotherapy, Net (polyhedron), Medicine, Artificial intelligence, Radiology, Software, Programming language, Mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
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2025: 5, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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14Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.targets | 13, 287 |
| abstract_inverted_index.trained | 54, 125, 194, 277, 302, 322 |
| abstract_inverted_index.treated | 79 |
| abstract_inverted_index.(MRIgRT) | 4 |
| abstract_inverted_index.Adaptive | 1 |
| abstract_inverted_index.Compared | 224 |
| abstract_inverted_index.accuracy | 105 |
| abstract_inverted_index.accurate | 6, 328 |
| abstract_inverted_index.analysis | 173 |
| abstract_inverted_index.bladder, | 270 |
| abstract_inverted_index.changes. | 36 |
| abstract_inverted_index.clinical | 98, 310, 330 |
| abstract_inverted_index.compared | 94 |
| abstract_inverted_index.complete | 216 |
| abstract_inverted_index.contours | 147 |
| abstract_inverted_index.distance | 116, 120 |
| abstract_inverted_index.metrics. | 122 |
| abstract_inverted_index.nnU-net, | 303 |
| abstract_inverted_index.obtained | 139 |
| abstract_inverted_index.patients | 57, 68, 189, 200 |
| abstract_inverted_index.practice | 311 |
| abstract_inverted_index.prostate | 62 |
| abstract_inverted_index.provided | 341 |
| abstract_inverted_index.requires | 5 |
| abstract_inverted_index.sigmoid. | 273 |
| abstract_inverted_index.transfer | 227 |
| abstract_inverted_index.workflow | 99, 219 |
| abstract_inverted_index.DIR-based | 313 |
| abstract_inverted_index.Hausdorff | 119 |
| abstract_inverted_index.MRI-linac | 84 |
| abstract_inverted_index.automatic | 43, 217 |
| abstract_inverted_index.developed | 39 |
| abstract_inverted_index.efficient | 8 |
| abstract_inverted_index.evaluated | 41, 107 |
| abstract_inverted_index.localized | 61, 72 |
| abstract_inverted_index.performed | 237 |
| abstract_inverted_index.prostate, | 73, 75 |
| abstract_inverted_index.segmented | 128 |
| abstract_inverted_index.variable, | 22 |
| abstract_inverted_index.variation | 263 |
| abstract_inverted_index.(localized | 196 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Conclusion | 274 |
| abstract_inverted_index.Especially | 198 |
| abstract_inverted_index.MRI-guided | 2 |
| abstract_inverted_index.MRI-linac. | 320 |
| abstract_inverted_index.Stratified | 172 |
| abstract_inverted_index.Therefore, | 37 |
| abstract_inverted_index.algorithm, | 234 |
| abstract_inverted_index.anatomical | 35 |
| abstract_inverted_index.clinically | 231 |
| abstract_inverted_index.deformable | 24 |
| abstract_inverted_index.framework. | 49 |
| abstract_inverted_index.metastatic | 74 |
| abstract_inverted_index.prostate). | 197 |
| abstract_inverted_index.segmenting | 284 |
| abstract_inverted_index.similarity | 111, 137 |
| abstract_inverted_index.structures | 131 |
| abstract_inverted_index.volumetric | 259 |
| abstract_inverted_index.(DIR)-based | 27 |
| abstract_inverted_index.coefficient | 112 |
| abstract_inverted_index.delineation | 218 |
| abstract_inverted_index.performance | 89, 181, 205 |
| abstract_inverted_index.propagation | 29, 315 |
| abstract_inverted_index.significant | 246 |
| abstract_inverted_index.structures, | 142 |
| abstract_inverted_index.structures. | 171 |
| abstract_inverted_index.surrounding | 213 |
| abstract_inverted_index.considerable | 258 |
| abstract_inverted_index.institution. | 87 |
| abstract_inverted_index.open-source. | 343 |
| abstract_inverted_index.radiotherapy | 3 |
| abstract_inverted_index.registration | 26 |
| abstract_inverted_index.segmentation | 9, 18, 44, 104 |
| abstract_inverted_index.successfully | 127, 276 |
| abstract_inverted_index.sufficiently | 325 |
| abstract_inverted_index.approximately | 221 |
| abstract_inverted_index.automatically | 283 |
| abstract_inverted_index.discrepancies | 163 |
| abstract_inverted_index.outperforming | 307 |
| abstract_inverted_index.statistically | 238 |
| abstract_inverted_index.time-consuming | 20 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5073947915, https://openalex.org/A5056342588 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I177969490, https://openalex.org/I2801498763 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6100000143051147 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.87944374 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |