Report
Evolving Horizons in Radiotherapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification
| العنوان: | Evolving Horizons in Radiotherapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification |
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| المؤلفون: | 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 |
| سنة النشر: | 2023 |
| المجموعة: | Physics (Other) |
| مصطلحات موضوعية: | Physics - Medical Physics |
| الوصف: | 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. Comment: 13 pages, 4 figures |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1016/j.adro.2024.101521 |
| URL الوصول: | http://arxiv.org/abs/2310.10867 |
| رقم الانضمام: | edsarx.2310.10867 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1016/j.adro.2024.101521 |
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