Segment Any Cell：一种基于 SAM 的自动提示微调细胞核分割框架
Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation
AI总结：
本文提出 SAC 框架，通过 LoRA 注意力适配与自动提示生成器增强 SAM，以提升医学影像中的细胞核分割性能并超越现有 SOTA 方法。
摘要：
在快速发展的 AI 研究领域，BERT 和 GPT 等基础模型显著推动了语言与视觉任务。ChatGPT 和 Segmentation Anything Model（SAM）等预训练-提示模型的出现进一步革新了图像分割。然而，它们在专业领域的应用，尤其是医学影像中的细胞核分割，暴露出一个关键挑战：生成高质量、信息丰富的提示，与在基础模型上应用最先进（SOTA）微调技术同样关键。为解决这一问题，我们提出 Segment Any Cell（SAC），这是一个专门增强 SAM 以用于细胞核分割的创新框架。SAC 在 Transformer 的注意力层中集成 Low-Rank Adaptation（LoRA）以改进微调过程，性能优于现有 SOTA 方法。它还引入了创新的自动提示生成器，可生成有效提示来引导分割，这是应对生物医学影像中细胞核分割复杂性的关键因素。大量实验证明了 SAC 在细胞核分割任务上的优越性，并证明其作为病理学家和研究人员工具的有效性。我们的贡献包括一种新颖的提示生成策略、对多种分割任务的自动化适应能力、Low-Rank Attention Adaptation 在 SAM 中的创新应用，以及一个应对语义分割挑战的通用框架。
英文摘要：
In the rapidly evolving field of AI research, foundational models like BERT and GPT have significantly advanced language and vision tasks. The advent of pretrain-prompting models such as ChatGPT and Segmentation Anything Model (SAM) has further revolutionized image segmentation. However, their applications in specialized areas, particularly in nuclei segmentation within medical imaging, reveal a key challenge: the generation of high-quality, informative prompts is as crucial as applying state-of-the-art (SOTA) fine-tuning techniques on foundation models. To address this, we introduce Segment Any Cell (SAC), an innovative framework that enhances SAM specifically for nuclei segmentation. SAC integrates a Low-Rank Adaptation (LoRA) within the attention layer of the Transformer to improve the fine-tuning process, outperforming existing SOTA methods. It also introduces an innovative auto-prompt generator that produces effective prompts to guide segmentation, a critical factor in handling the complexities of nuclei segmentation in biomedical imaging. Our extensive experiments demonstrate the superiority of SAC in nuclei segmentation tasks, proving its effectiveness as a tool for pathologists and researchers. Our contributions include a novel prompt generation strategy, automated adaptability for diverse segmentation tasks, the innovative application of Low-Rank Attention Adaptation in SAM, and a versatile framework for semantic segmentation challenges.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
