MIT researchers have developed MultiverSeg, an interactive AI tool enabling efficient, user-driven segmentation of biomedical image datasets without prior model training.
Key Details
- 1MultiverSeg allows users to annotate images through clicks and scribbles, reducing manual input over time.
- 2The system does not require presegmented data or machine learning expertise for new tasks.
- 3By the ninth image, only two user interactions are needed for accurate segmentation, outperforming existing tools.
- 4Applicable across imaging types such as X-ray and adaptable to a range of biomedical image datasets.
- 5Supported by Quanta Computer and the NIH, and benchmarked against state-of-the-art segmentation tools.
Why It Matters
Manual image annotation is a major bottleneck in radiology and clinical research. This tool could significantly expedite dataset preparation for studies and AI development, lowering expertise and resource barriers and potentially accelerating clinical translation.
Source
EurekAlert
Related News
•EurekAlert
AI Model Predicts Growth Spurts from Pediatric Neck X-rays for Orthodontics
Korean researchers developed an AI system (ARNet-v2) that predicts children's growth spurts from neck X-rays to enhance orthodontic treatment planning.
•EurekAlert
Dana-Farber Showcases AI and Clinical Trial Advances at ESMO 2025
Dana-Farber researchers present major cancer clinical trial results, including AI-driven data analysis, at ESMO Congress 2025.
•EurekAlert
Einstein College Awarded $18M NIH Grant to Develop AI Tools for Mental Health Crisis Prediction
Albert Einstein College of Medicine received an $18 million NIH grant to create AI-based tools for predicting mental health crises using cognitive monitoring.