Pre-trained on a large and diverse dataset, the segment anything model (SAM) is the first promptable foundation model in computer vision aiming at object segmentation tasks. In this work, we evaluate SAM for the task of nuclear instance segmentation performance with zero-shot learning and finetuning. We compare SAM with other representative methods in nuclear instance segmentation, especially in the context of model generalisability. To achieve automatic nuclear instance segmentation, we propose using a nuclei detection model to provide bounding boxes or central points of nu-clei as visual prompts for SAM in generating nuclear instance masks from histology images.

翻译：预先在大规模多样化数据集上训练的分割任意模型（SAM）是计算机视觉中首个旨在实现目标分割任务的可提示基础模型。本研究通过零样本学习和微调两种方式，评估了SAM在细胞核实例分割任务中的性能。我们将SAM与细胞核实例分割中的其他代表性方法进行比较，特别关注模型泛化性这一背景。为实现自动细胞核实例分割，我们提出利用细胞核检测模型提供边界框或中心点作为SAM的视觉提示，从而从组织学图像中生成细胞核实例掩码。