Evolving Horizons in Radiotherapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866913463503159296 |
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| author | Wahid, Kareem A. Cardenas, Carlos E. Marquez, Barbara Netherton, Tucker J. Kann, Benjamin H. Court, Laurence E. He, Renjie Naser, Mohamed A. Moreno, Amy C. Fuller, Clifton D. Fuentes, David |
| author_facet | Wahid, Kareem A. Cardenas, Carlos E. Marquez, Barbara Netherton, Tucker J. Kann, Benjamin H. Court, Laurence E. He, Renjie Naser, Mohamed A. Moreno, Amy C. Fuller, Clifton D. Fuentes, David |
| contents | Deep learning has significantly advanced the potential for automated contouring in radiotherapy planning. In this manuscript, guided by contemporary literature, we underscore three key insights: (1) High-quality training data is essential for auto-contouring algorithms; (2) Auto-contouring models demonstrate commendable performance even with limited medical image data; (3) The quantitative performance of auto-contouring is reaching a plateau. Given these insights, we emphasize the need for the radiotherapy research community to embrace data-centric approaches to further foster clinical adoption of auto-contouring technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_10867 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Evolving Horizons in Radiotherapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification Wahid, Kareem A. Cardenas, Carlos E. Marquez, Barbara Netherton, Tucker J. Kann, Benjamin H. Court, Laurence E. He, Renjie Naser, Mohamed A. Moreno, Amy C. Fuller, Clifton D. Fuentes, David Medical Physics Deep learning has significantly advanced the potential for automated contouring in radiotherapy planning. In this manuscript, guided by contemporary literature, we underscore three key insights: (1) High-quality training data is essential for auto-contouring algorithms; (2) Auto-contouring models demonstrate commendable performance even with limited medical image data; (3) The quantitative performance of auto-contouring is reaching a plateau. Given these insights, we emphasize the need for the radiotherapy research community to embrace data-centric approaches to further foster clinical adoption of auto-contouring technologies. |
| title | Evolving Horizons in Radiotherapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2310.10867 |