Summary of Weakly Supervised Segmentation Of Vertebral Bodies with Iterative Slice-propagation, by Shiqi Peng et al.
Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-propagation
by Shiqi Peng, Bolin Lai, Guangyu Yao, Xiaoyun Zhang, Ya Zhang, Yan-Feng Wang, Hui Zhao
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Weakly Supervised Iterative Spinal Segmentation (WISS) method that leverages weak labels on a single sagittal slice to achieve automatic volumetric segmentation for vertebral bodies (VBs) from CT images. WISS iteratively trains and refines labels in the training set, then segments VBs slice by slice using a slice-propagation method. The approach achieves distinct improvements in spine metastases detection and saves labeling costs while sacrificing little performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find specific parts of the spine from CT scans. This is a tough task because it requires a lot of work to label each part correctly. In this paper, scientists created an algorithm that can do this job with less effort. The algorithm uses weak labels on just one picture of the spine and then applies it to the whole scan. This makes it faster and cheaper to get the same results as before. |
Keywords
* Artificial intelligence * Supervised
