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The homogenate obtained after cell lysis contains various membrane-bound organelles that can be further separated into pure fractions by subcellular fractionation. These isolates are used to study specific cellular components, analyze localized protein activity, and are even employed in diagnostics. Fractionation is typically achieved using centrifugation methods, the most common being density-gradient and differential centrifugation.
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Related Experiment Video

Updated: Apr 30, 2026

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
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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
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    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.