Summary of Automated Lesion Segmentation in Whole-body Pet/ct in a Multitracer Setting, by Qiaoyi Xue et al.
Automated Lesion Segmentation in Whole-Body PET/CT in a multitracer setting
by Qiaoyi Xue, Youdan Feng, Jiayi Liu, Tianming Xu, Kaixin Shen, Chuyun Shen, Yuhang Shi
First submitted to arxiv on: 15 Sep 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 The proposed workflow leverages YOLOv8 for data classification to preprocess FDG and PSMA PET/CT images separately, ultimately enhancing lesion segmentation accuracy. The study assesses the performance of this automated segmentation workflow for multitracer PET images, with implications for improving diagnostic workflows and patient-specific treatment plans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to develop a better way to automatically identify lesions in medical imaging scans. By using a specific algorithm (YOLOv8) to classify the data, the team hopes to improve the accuracy of identifying these lesions in different types of scans. The goal is to make diagnostic processes more efficient and patient-specific treatment plans more effective. |
Keywords
» Artificial intelligence » Classification
