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.

通过在大规模多样化数据集上进行预训练，SAM 模型是计算机视觉领域针对目标分割任务的第一个可提示的基础模型。本文评估了 SAM 在零样本学习和微调下的核实例分割性能，将其与其他代表性方法在核实例分割中尤其是模型泛化能力的背景下进行比较，并提出使用核实例检测模型来为 SAM 提供核实例的边界框或中心点作为视觉提示，从组织学图像中生成核实例掩模。

关于组织学图像中核实例分割的区段任何模型的普适性

On generalisability of segment anything model for nuclear instance  segmentation in histology images

Nuclear instance segmentation in histology images is crucial for a broad
spectrum of clinical applications. Current prevailing nuclear instance
segmentation algorithms rely on regression of nuclei contours, distance maps,
watershed markers or a proxy nuclear representation of star-convex polygons.
Consequently, these methods necessitate sophisticated post-processing
operations to distinguish nuclei instances, which are commonly acknowledged to
be error-prone and parameter-sensitive. Recently, the segment anything model
(SAM) has earned attracted huge attention within the domain of medical image
segmentation due to its impressive generalization ability and promptable
property. Nevertheless, its potential on nuclear instance segmentation remains
largely underexplored. In this paper, we present a novel prompt-driven
framework that consists of a point prompter and a SAM for automatic nuclei
instance segmentation. Specifically, the prompter learns to generate a unique
point prompt for each nucleus while the SAM is fine tuned to output the
corresponding mask of the cued nucleus. Furthermore, we propose to add adjacent
nuclei as negative prompts to promote the model's ability to recognize
overlapping nuclei. Without bells and whistles, our proposed method sets a new
state-of-the-art performance on three challenging benchmarks. Our code is
available at
\textcolor{magenta}{https://github.com/windygoo/PromptNucSeg} .

本研究提出了一种新颖的基于提示的框架，包括一个点提示器和一个用于自动核实例分割的段装置模型。通过学习为每个细胞核生成唯一的点提示，并将段装置模型微调为输出相应的细胞核掩模，我们的方法在三个具有挑战性的基准测试上取得了最新的性能。