Emerging foundation models in machine learning are models trained on vast
amounts of data that have been shown to generalize well to new tasks. Often
these models can be prompted with multi-modal inputs that range from natural
language descriptions over images to point clouds. In this paper, we propose
topological data analysis (TDA) guided prompt optimization for the Segment
Anything Model (SAM) and show preliminary results in the biological image
segmentation domain. Our approach replaces the standard grid search approach
that is used in the original implementation and finds point locations based on
their topological significance. Our results show that the TDA optimized point
cloud is much better suited for finding small objects and massively reduces
computational complexity despite the extra step in scenarios which require many
segmentations.

本文提出了使用拓扑数据分析引导的 Prompt 优化方法来优化 Segment Anything Model（SAM）模型，这种方法可以在生物图像分割领域得到初步结果，并且在需要大量分割的情况下，可以大大降低计算复杂度。

基于拓扑数据分析指导的零样本生物成像分割

Topological Data Analysis Guided Segment Anything Model Prompt  Optimization for Zero-Shot Segmentation in Biological Imaging

For many biological image segmentation tasks, including topological
knowledge, such as the nesting of classes, can greatly improve results.
However, most `out-of-the-box' CNN models are still blind to such prior
information. In this paper, we propose a novel approach to encode this
information, through a multi-level activation layer and three compatible
losses. We benchmark all of them on nuclei segmentation in bright-field
microscopy cell images from the 2018 Data Science Bowl challenge, offering an
exemplary segmentation task with cells and nested subcellular structures. Our
scheme greatly speeds up learning, and outperforms standard multi-class
classification with soft-max activation and a previously proposed method
stemming from it, improving the Dice score significantly (p-values<0.007). Our
approach is conceptually simple, easy to implement and can be integrated in any
CNN architecture. It can be generalized to a higher number of classes, with or
without further relations of containment.

本研究提出一种新颖的方法，通过多层激活层和三种兼容的损失来编码基于拓扑学的先验信息，以解决生物图像分割任务中的问题，并在 2018 年的 Data Science Bowl 挑战中验证了该方法的效果，可以大幅提高分割的 Dice 分数。