Summary of Deep Learning-based Auto-segmentation Of Paraganglioma For Growth Monitoring, by E.m.c. Sijben et al.
Deep learning-based auto-segmentation of paraganglioma for growth monitoring
by E.M.C. Sijben, J.C. Jansen, M. de Ridder, P.A.N. Bosman, T. Alderliesten
First submitted to arxiv on: 19 Mar 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 This paper proposes an automated tumor volume measurement method for paragangliomas, a rare neuroendocrine tumor. The existing methods are time-consuming and prone to observer variability. The goal is to enable long-term monitoring and modeling of tumor growth, which could improve treatment decisions and prevent unnecessary side effects. The proposed method uses a deep learning segmentation model called no-new-UNnet (nnUNet) and achieves performance comparable to manual delineation by multiple observers. Additionally, the paper demonstrates how the model can be used to track tumor volume over time and update growth function fits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors better measure the size of a rare type of tumor that forms in the head and neck. Right now, doing this measurement takes a lot of time and is not very accurate because it relies on assumptions about the shape of the tumor. The goal is to create a system that can automatically measure the tumor’s volume and track its growth over time. This would help doctors decide when to give patients treatment and avoid giving them unnecessary treatment that could cause harm. The researchers used a special kind of artificial intelligence called deep learning to develop a new method for measuring the tumor’s volume, which is just as accurate as what doctors do by hand. |
Keywords
* Artificial intelligence * Deep learning
