mlsanityData to 14 Oct 2026

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

arXiv 2506.02783 ↗First posted 3 Jun 2025cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 30 references that are in the map. First posted on arXiv 3 Jun 2025.

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7 of 30 references.

References · 7

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2023Segment Anythingcited by 8,234
  3. 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
  4. 2023EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anythingcited by 188
  5. 2018Cell Detection with Star-convex Polygonscited by 183
  6. 2023The Multi-modality Cell Segmentation Challenge: Towards Universal Solutionscited by 39
  7. 2023MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysiscited by 1

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