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.

翻译：在人工智能研究快速发展的领域中，像BERT和GPT这样的基础模型显著推动了语言和视觉任务的进步。诸如ChatGPT和分割一切模型（SAM）等预训练-提示模型的问世进一步革新了图像分割技术。然而，它们在医学影像中细胞核分割等专业领域的应用揭示了一个关键挑战：生成高质量、信息丰富的提示与在基础模型上应用最先进的（SOTA）微调技术同等重要。为解决这一问题，我们提出了分割任意细胞（SAC）——一种专门针对细胞核分割增强SAM的创新框架。SAC在Transformer的注意力层集成了低秩适应（LoRA）以改进微调过程，其性能超越了现有SOTA方法。它还引入了一种创新的自动提示生成器，可产生有效提示以指导分割，这是处理生物医学成像中细胞核分割复杂性的关键因素。我们的大量实验证明了SAC在细胞核分割任务中的优越性，验证了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：新颖的提示生成策略、针对多样化分割任务的自动适应性、SAM中低秩注意力适应的创新应用，以及一个用于语义分割挑战的通用框架。