The Segment Anything Model (SAM) is a recently proposed prompt-based
segmentation model in a generic zero-shot segmentation approach. With the
zero-shot segmentation capacity, SAM achieved impressive flexibility and
precision on various segmentation tasks. However, the current pipeline requires
manual prompts during the inference stage, which is still resource intensive
for biomedical image segmentation. In this paper, instead of using prompts
during the inference stage, we introduce a pipeline that utilizes the SAM,
called all-in-SAM, through the entire AI development workflow (from annotation
generation to model finetuning) without requiring manual prompts during the
inference stage. Specifically, SAM is first employed to generate pixel-level
annotations from weak prompts (e.g., points, bounding box). Then, the
pixel-level annotations are used to finetune the SAM segmentation model rather
than training from scratch. Our experimental results reveal two key findings:
1) the proposed pipeline surpasses the state-of-the-art (SOTA) methods in a
nuclei segmentation task on the public Monuseg dataset, and 2) the utilization
of weak and few annotations for SAM finetuning achieves competitive performance
compared to using strong pixel-wise annotated data.

本文提出了一个称为 all-in-SAM 的流程，通过将 SAM 用于整个 AI 开发工作流（从注释生成到模型微调），而无需在推理阶段使用手动提示，取代了在推理阶段使用提示的方法，可以在不需要手动提示的情况下更好地完成生物医学图像分割任务，并在公共数据集 Monuseg 上的一个核分割任务中超越了最先进的方法，并且使用弱注释进行 SAM 微调的实验结果表明其性能相对于使用强像素注释数据的方法而言具有竞争力。

All-in-SAM：从弱注释到基于提示微调的像素级细胞核分割

All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with  Prompt-based Finetuning

Due to the wide existence and large morphological variances of nuclei,
accurate nuclei instance segmentation is still one of the most challenging
tasks in computational pathology. The annotating of nuclei instances, requiring
experienced pathologists to manually draw the contours, is extremely laborious
and expensive, which often results in the deficiency of annotated data. The
deep learning based segmentation approaches, which highly rely on the quantity
of training data, are difficult to fully demonstrate their capacity in this
area. In this paper, we propose a novel self-supervised learning framework to
deeply exploit the capacity of widely-used convolutional neural networks (CNNs)
on the nuclei instance segmentation task. The proposed approach involves two
sub-tasks (i.e., scale-wise triplet learning and count ranking), which enable
neural networks to implicitly leverage the prior-knowledge of nuclei size and
quantity, and accordingly mine the instance-aware feature representations from
the raw data. Experimental results on the publicly available MoNuSeg dataset
show that the proposed self-supervised learning approach can remarkably boost
the segmentation accuracy of nuclei instance---a new state-of-the-art average
Aggregated Jaccard Index (AJI) of 70.63%, is achieved by our self-supervised
ResUNet-101. To our best knowledge, this is the first work focusing on the
self-supervised learning for instance segmentation.

本文提出了一种自监督学习框架，通过对卷积神经网络进行尺度三元组学习和数量排序等两个子任务，隐式地利用细胞核大小和数量的先验知识，从原始数据中挖掘出特征表示，最终在公开数据集上取得了非常好的结果，大大提高了细胞核实例分割的准确性。