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。

释放基于提示驱动的核实例分割的力量

Unleashing the Power of Prompt-driven Nucleus Instance Segmentation

In computational pathology, automatic nuclei instance segmentation plays an
essential role in whole slide image analysis. While many computerized
approaches have been proposed for this task, supervised deep learning (DL)
methods have shown superior segmentation performances compared to classical
machine learning and image processing techniques. However, these models need
fully annotated datasets for training which is challenging to acquire,
especially in the medical domain. In this work, we release one of the biggest
fully manually annotated datasets of nuclei in Hematoxylin and Eosin
(H&E)-stained histological images, called NuInsSeg. This dataset contains 665
image patches with more than 30,000 manually segmented nuclei from 31 human and
mouse organs. Moreover, for the first time, we provide additional ambiguous
area masks for the entire dataset. These vague areas represent the parts of the
images where precise and deterministic manual annotations are impossible, even
for human experts. The dataset and detailed step-by-step instructions to
generate related segmentation masks are publicly available at
this https URL and
this https URL, respectively.

我们发布了一个大规模的手工标注的细胞核数据集 NuInsSeg，其中包含来自 31 个人和小鼠器官的 665 个图像补丁，超过 30,000 个手动分割的细胞核。此外，我们首次提供了整个数据集的模糊区域掩模，用于表示图像的某些部分即使对于人类专家来说也无法精确和确定地进行手动注释。