Cellpose+拓展细胞分割功能，自动提取染色细胞形态特征
Cellpose+, a morphological analysis tool for feature extraction of stained cell images
- 基于Cellpose框架增强形态特征提取能力
- 在DAPI/FITC染色细胞数据集上实现高效分析
- 适合需要自动化细胞形态研究的生物医学工作者
先进的图像分割与处理工具为研究细胞过程及其动态提供了可能。然而，图像分析往往重复性高且耗时。当前，基于深度学习的数据驱动方法有望实现自动化、精准且快速的图像分析。本文扩展了Cellpose这一前沿细胞分割框架的应用，新增形态特征提取功能，用于评估细胞形态特性。同时，我们构建了一个包含DAPI和FITC染色细胞的数据集，并将新方法应用于该数据集。
原文摘要 · Abstract (English)
Advanced image segmentation and processing tools present an opportunity to study cell processes and their dynamics. However, image analysis is often routine and time-consuming. Nowadays, alternative data-driven approaches using deep learning are potentially offering automatized, accurate, and fast image analysis. In this paper, we extend the applications of Cellpose, a state-of-the-art cell segmentation framework, with feature extraction capabilities to assess morphological characteristics. We also introduce a dataset of DAPI and FITC stained cells to which our new method is applied.
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