Summary of Multiverseg: Scalable Interactive Segmentation Of Biomedical Imaging Datasets with In-context Guidance, by Hallee E. Wong and Jose Javier Gonzalez Ortiz and John Guttag and Adrian V. Dalca
MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance
by Hallee E. Wong, Jose Javier Gonzalez Ortiz, John Guttag, Adrian V. Dalca
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 MultiverSeg system enables medical researchers and clinicians to rapidly segment new datasets without requiring existing labeled data. This model takes user interactions such as clicks, bounding boxes, or scribbles as input and predicts a segmentation. As users segment more images, those images and segmentations become additional inputs, providing context that reduces the number of interactions required for each new image. MultiverSeg outperforms state-of-the-art interactive segmentation methods by 53% in scribble steps and 36% in clicks while achieving 90% Dice on unseen tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team developed a way to help medical experts quickly identify important parts of new images. They created a system that can learn from the expert’s actions as they segment more images. This system, called MultiverSeg, gets better at predicting what’s in an image as it gets more training data. It works faster and more accurately than other methods, reducing the time and effort needed to get good results. |
