Summary of Brain Tumor Segmentation in Mri Images with 3d U-net and Contextual Transformer, by Thien-qua T. Nguyen et al.
Brain Tumor Segmentation in MRI Images with 3D U-Net and Contextual Transformer
by Thien-Qua T. Nguyen, Hieu-Nghia Nguyen, Thanh-Hieu Bui, Thien B. Nguyen-Tat, Vuong M. Ngo
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 research presents an advanced approach to precise brain tumor segmentation in MRI scans using a 3D-UNet model combined with a Context Transformer (CoT). The proposed architecture extends the CoT to a 3D format, integrating it with the base model to utilize contextual information in MRI scans. This enhances feature extraction and facilitates precise capture of detailed tumor mass structures, including location, size, and boundaries. Experimental results demonstrate the outstanding segmentation performance of the proposed method, achieving Dice scores of 82.0%, 81.5%, and 89.0% for Enhancing Tumor, Tumor Core, and Whole Tumor on BraTS2019. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new way to accurately detect brain tumors in MRI scans using a special kind of computer program called an advanced 3D-UNet model. The program combines two key parts: the base model and something called Context Transformer (CoT). This combination helps the program learn how different parts of the tumor relate to each other, making it better at finding the exact shape, size, and location of the tumor. The results show that this new approach is much better than current methods, with a high accuracy rate on a dataset from BraTS2019. |
Keywords
» Artificial intelligence » Feature extraction » Transformer » Unet
