SAMJ: Fast Image Annotation on ImageJ/Fiji via Segment Anything Model

2025/06/03 by Carlos Garcia-Lopez-de-Haro, Garcia-Lopez-de-Haro, Carlos, Caterina Fuster-Barcelo +21 · 1 voice · 1 citation
#cs.CV

paper · pdf · doi:10.48550/arxiv.2506.02783

Abstract

Mask annotation remains a significant bottleneck in AI-driven biomedical image analysis due to its labor-intensive nature. To address this challenge, we introduce SAMJ, a user-friendly ImageJ/Fiji plugin leveraging the Segment Anything Model (SAM). SAMJ enables seamless, interactive annotations with one-click installation on standard computers. Designed for real-time object delineation in large scientific images, SAMJ is an easy-to-use solution that simplifies and accelerates the creation of labeled image datasets.

Cited by

• Host plant quality reshapes symbiotic organ architecture without altering symbiont density

Discussions

• 🚀 Manual mask annotation slowing you down? 💡 SAMJ is a new ImageJ/Fiji plugin using Segment Anything (SAM) for fast, interactive segmentation — no GPU needed, one-click install, real-time results. � [bsky, 55 points, 1 comments]

Related

• \mathrmSAM^Med: A medical image annotation framework based on large vision model
• Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model
• SAM on Medical Images: A Comprehensive Study on Three Prompt Modes
• How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images
• Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey