Summary of Deep Learning-based Auto-segmentation Of Planning Target Volume For Total Marrow and Lymph Node Irradiation, by Ricardo Coimbra Brioso et al.
Deep Learning-Based Auto-Segmentation of Planning Target Volume for Total Marrow and Lymph Node Irradiation
by Ricardo Coimbra Brioso, Damiano Dei, Nicola Lambri, Daniele Loiacono, Pietro Mancosu, Marta Scorsetti
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the application of Deep Learning (DL) for automating the segmentation of Planning Target Volume (PTV) in Total Marrow and Lymph Node Irradiation (TMLI) treatments. The goal is to optimize radiotherapy delivery, as manual contouring is time-consuming and prone to errors. Building upon previous work, the authors develop both 2D and 3D U-Net models using the nnU-Net framework, demonstrating statistically significant improvements in segmentation performance. The study also evaluates model robustness on challenging areas of the target volume, showcasing a viable and scalable solution for increasing patient access to TMLI treatment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses AI to help doctors better prepare patients for cancer treatment. This is important because current methods are slow and can be wrong. Researchers used special kinds of artificial intelligence called Deep Learning models to try to fix this problem. They created new models that could look at images of the body and identify the areas that need radiation therapy. The results show that these AI models are much better than the old way of doing things, and they can even work well on the hardest parts of the treatment plan. This is an important step forward in using AI to help people with cancer. |
Keywords
* Artificial intelligence * Deep learning
