Summary of Tractoembed: Modular Multi-level Embedding Framework For White Matter Tract Segmentation, by Anoushkrit Goel et al.
TractoEmbed: Modular Multi-level Embedding framework for white matter tract segmentation
by Anoushkrit Goel, Bipanjit Singh, Ankita Joshi, Ranjeet Ranjan Jha, Chirag Ahuja, Aditya Nigam, Arnav Bhavsar
First submitted to arxiv on: 12 Nov 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 paper proposes a novel multi-level embedding framework called TractoEmbed for white matter tract segmentation. TractoEmbed addresses challenges like class imbalance and structural similarity by encoding localized representations through learning tasks in respective encoders. The framework introduces a hierarchical streamline data representation that captures maximum spatial information at each level, outperforming state-of-the-art methods on different datasets across various age groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors better understand how brain structures are connected and plan surgeries more accurately. It develops a new way to look at brain fibers called TractoEmbed. This method is good at separating fibers with similar shapes from each other, even when they’re very different in size or shape. The results show that this new approach works well on various datasets of brain scans, and can be improved further by adding more information. |
Keywords
» Artificial intelligence » Embedding
