Summary of Clicksam: Fine-tuning Segment Anything Model Using Click Prompts For Ultrasound Image Segmentation, by Aimee Guo et al.
ClickSAM: Fine-tuning Segment Anything Model using click prompts for ultrasound image segmentation
by Aimee Guo, Grace Fei, Hemanth Pasupuleti, Jing Wang
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Medical Physics (physics.med-ph)
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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 Segment Anything Model (SAM) is a prominent tool in image processing due to its exceptional accuracy, versatility, and efficient design. However, the current SAM model is trained on a diverse dataset that is not specifically designed for medical images, particularly ultrasound images. Ultrasound images often contain noise, making it challenging to segment out crucial structures. This project introduces ClickSAM, which fine-tunes the Segment Anything Model using click prompts tailored to ultrasound images. ClickSAM undergoes two stages of training: the first stage is trained on single-click prompts centered in ground-truth contours, and the second stage focuses on improving performance through additional positive and negative click prompts. By comparing predictions with ground-truth masks, true positives, false positives, and false negatives are calculated. Positive clicks are generated using true positives and false negatives, while negative clicks are generated using false positives. The Centroidal Voronoi Tessellation algorithm is employed to collect positive and negative click prompts in each segment, enhancing the model’s performance during the second stage of training. With click-train methods, ClickSAM demonstrates superior performance compared to existing models for ultrasound image segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This project makes a popular tool called SAM work better with medical images from ultrasound machines. Right now, SAM is great at recognizing things in pictures, but it struggles with noisy ultrasound images that have lots of noise. To solve this problem, the researchers created ClickSAM, which teaches SAM to do better using special instructions (called click prompts) designed just for ultrasound images. ClickSAM has two steps: the first step trains SAM on simple instructions, and the second step makes SAM even better by adding more information. By comparing what SAM thinks with what’s really there, they can tell how good it is. They’re able to make SAM work much better than before! |
Keywords
» Artificial intelligence » Image segmentation » Sam
