Summary of Umambaadj: Advancing Gtv Segmentation For Head and Neck Cancer in Mri-guided Rt with Umamba and Nnu-net Resenc Planner, by Jintao Ren et al.
UMambaAdj: Advancing GTV Segmentation for Head and Neck Cancer in MRI-Guided RT with UMamba and nnU-Net ResEnc Planner
by Jintao Ren, Kim Hochreuter, Jesper Folsted Kallehauge, Stine Sofia Korreman
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Medical Physics (physics.med-ph)
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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 proposed ‘UMambaAdj’ approach integrates two deep learning segmentation innovations, UMamba and nnU-Net Residual Encoder (ResEnc), to accurately segment the gross tumor volume (GTV) in Magnetic Resonance Imaging (MRI)-guided adaptive radiotherapy for head and neck cancer (HNC). The method leverages long-range dependencies and multistage residual blocks to enhance feature extraction. Evaluation on the HNTS-MRG 2024 challenge test set using pre-RT T2-weighted MRI images achieved an aggregated Dice Similarity Coefficient (DSCagg) of 0.751 for GTVp, 0.842 for GTVn, and a mean DSCagg of 0.796. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a better way to use magnetic resonance imaging (MRI) in cancer treatment. MRI helps doctors guide radiation therapy to make sure the right amount of radiation reaches the tumor. The problem is that it’s hard to accurately identify where the tumor is using just an MRI scan. This study combines two new techniques, UMamba and nnU-Net Residual Encoder, to improve the accuracy of this process. The results show that this approach works well, which could lead to better treatment outcomes for patients with head and neck cancer. |
Keywords
» Artificial intelligence » Deep learning » Encoder » Feature extraction
