Summary of Topology-preserving Deep Image Segmentation, by Xiaoling Hu et al.
Topology-Preserving Deep Image Segmentation
by Xiaoling Hu, Li Fuxin, Dimitris Samaras, Chao Chen
First submitted to arxiv on: 12 Jun 2019
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computational Geometry (cs.CG)
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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 novel method learns to segment with correct topology by designing a continuous-valued loss function that enforces a segmentation to have the same topology as the ground truth, specifically the same Betti number. This differentiable loss function is incorporated into end-to-end training of a deep neural network. The approach achieves better performance on topological correctness metrics such as the Betti number error, Adjusted Rand Index, and Variation of Information. It demonstrates superior results on natural and biomedical datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make computer programs better at recognizing patterns in images or other data by making sure they get the details right. Normally, these programs can mess up when it comes to tiny parts of the image, but this new method helps them stay accurate. The program uses a special kind of math problem that makes sure the result is correct, and it’s very good at getting things right. It works well on lots of different types of data, from natural images to medical pictures. |
Keywords
* Artificial intelligence * Loss function * Neural network
