Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study Article Swipe
Dazhou Guo
,
Jia Ge
,
Xianghua Ye
,
Senxiang Yan
,
Xin Yi
,
Yuchen Song
,
Bing‐Shen Huang
,
Tsung‐Min Hung
,
Zhuotun Zhu
,
Ling Peng
,
Yanping Ren
,
Rui Liu
,
Gong Zhang
,
Mengyuan Mao
,
Xiaohua Chen
,
Zhongjie Lu
,
Wenxiang Li
,
Yuzhen Chen
,
Lingyun Huang
,
Jing Xiao
,
Adam P. Harrison
,
Le Lü
,
Chien‐Yu Lin
,
Dakai Jin
,
Tsung‐Ying Ho
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.01544
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.01544
Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications. Consensus guidelines recommend a set of more than 40 OARs in the head and neck (H&N) region, however, due to the predictable prohibitive labor-cost of this task, most institutions choose a substantially simplified protocol by delineating a smaller subset of OARs and neglecting the dose distributions associated with other OARs. In this work we propose a novel, automated and highly effective stratified OAR segmentation (SOARS) system using deep learning to precisely delineate a comprehensive set of 42 H&N OARs. SOARS stratifies 42 OARs into anchor, mid-level, and small & hard subcategories, with specifically derived neural network architectures for each category by neural architecture search (NAS) principles. We built SOARS models using 176 training patients in an internal institution and independently evaluated on 1327 external patients across six different institutions. It consistently outperformed other state-of-the-art methods by at least 3-5% in Dice score for each institutional evaluation (up to 36% relative error reduction in other metrics). More importantly, extensive multi-user studies evidently demonstrated that 98% of the SOARS predictions need only very minor or no revisions for direct clinical acceptance (saving 90% radiation oncologists workload), and their segmentation and dosimetric accuracy are within or smaller than the inter-user variation. These findings confirmed the strong clinical applicability of SOARS for the OAR delineation process in H&N cancer radiotherapy workflows, with improved efficiency, comprehensiveness, and quality.
Related Topics
Concepts
Workflow
Workload
Segmentation
Head and neck
Artificial intelligence
Computer science
Head and neck cancer
Scale (ratio)
Task (project management)
Feature (linguistics)
Radiation therapy
Medicine
Medical physics
Radiology
Cartography
Surgery
Geography
Engineering
Linguistics
Philosophy
Operating system
Systems engineering
Database
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2111.01544
- https://arxiv.org/pdf/2111.01544
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309443406
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4309443406Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2111.01544Digital Object Identifier
- Title
-
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional StudyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-01Full publication date if available
- Authors
-
Dazhou Guo, Jia Ge, Xianghua Ye, Senxiang Yan, Xin Yi, Yuchen Song, Bing‐Shen Huang, Tsung‐Min Hung, Zhuotun Zhu, Ling Peng, Yanping Ren, Rui Liu, Gong Zhang, Mengyuan Mao, Xiaohua Chen, Zhongjie Lu, Wenxiang Li, Yuzhen Chen, Lingyun Huang, Jing Xiao, Adam P. Harrison, Le Lü, Chien‐Yu Lin, Dakai Jin, Tsung‐Ying HoList of authors in order
- Landing page
-
https://arxiv.org/abs/2111.01544Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2111.01544Direct 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/2111.01544Direct OA link when available
- Concepts
-
Workflow, Workload, Segmentation, Head and neck, Artificial intelligence, Computer science, Head and neck cancer, Scale (ratio), Task (project management), Feature (linguistics), Radiation therapy, Medicine, Medical physics, Radiology, Cartography, Surgery, Geography, Engineering, Linguistics, Philosophy, Operating system, Systems engineering, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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