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Related Concept Videos

  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Artificial Intelligence
  9. Natural Language Processing
  11. Segment Any Cell: A Sam-based Auto-prompting Fine-tuning Framework For Nuclei Segmentation.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Artificial Intelligence
  9. Natural Language Processing
  11. Segment Any Cell: A Sam-based Auto-prompting Fine-tuning Framework For Nuclei Segmentation.

Related Experiment Video

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
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SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

523

Segment Any Cell: A SAM-Based Auto-Prompting Fine-Tuning Framework for Nuclei Segmentation.

Saiyang Na, Yuzhi Guo, Feng Jiang

    IEEE Transactions on Neural Networks and Learning Systems
    |October 27, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Segment Any Cell (SAC) enhances AI-driven nuclei segmentation in medical images. This framework improves prompt generation and fine-tuning for more accurate cell segmentation, aiding pathologists.

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    Area of Science:

    • Artificial Intelligence
    • Medical Imaging
    • Computational Biology

    Background:

    • Foundation models like BERT and GPT have advanced AI tasks.
    • Pretrain-prompting models, including ChatGPT and Segment Anything Model (SAM), have revolutionized image segmentation.
    • Effective prompt generation is critical for specialized segmentation tasks, especially in medical imaging.

    Purpose of the Study:

    • To introduce Segment Any Cell (SAC), a framework designed to enhance SAM for nuclei segmentation.
    • To address the challenge of generating high-quality prompts for nuclei segmentation in biomedical imaging.
    • To improve the fine-tuning process for foundation models in specialized medical imaging applications.

    Main Methods:

    • Integration of Low-Rank Adaptation (LoRA) within the Transformer's attention layer for improved fine-tuning.
  • Development of an innovative auto-prompt generator to create effective segmentation prompts.
  • Application of SAC to nuclei segmentation tasks in biomedical imaging.

    • Main Results:

    • SAC demonstrates superior performance in nuclei segmentation compared to existing state-of-the-art (SOTA) methods.
    • The framework effectively handles the complexities of nuclei segmentation in biomedical imaging.
    • Experimental results validate the effectiveness of SAC for pathologists and researchers.

    Conclusions:

    • SAC offers a novel prompt generation strategy and automated adaptability for diverse segmentation tasks.
    • The framework showcases the innovative application of low-rank attention adaptation in SAM.
    • SAC provides a versatile solution for both semantic and instance nuclei segmentation challenges.