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.

通过引入改进的像素级图像分割模型 Segment Any Cell (SAC)，结合自动生成的实用提示和先进的 Fine-Tuning 技术，在核分割领域实现了优于现有 SOTA 方法的性能提升。