YeastSAM

<div align="center"> <img src="src/yeastsam.png" alt="YeastSAM Logo" width="200"/> </div>

YeastSAM is a model and a framework for yeast cell analysis and mask processing. It provides an intuitive GUI launcher and various tools for generating masks, image registration, outline conversion, and mother-bud pair separation. For detailed documentation, visit YeastSAM.

Quick Start

If you just want to run YeastSAM, you can refer to µSAM to install the framework. We provide custom weights for better yeast cell segmentation that can be downloaded from our GitHub Releases. The custom weights can be used with napari, BAND, CLI, and QuPath.

Dataset

The dataset used for training and validation can be found at Zenodo: https://zenodo.org/records/17204942, including DIC and the masks.

Installation

Prerequisite: Conda

YeastSAM requires a Conda environment. If you do not already have Conda or Miniconda installed, please install one of them first:

• Conda or Miniconda

With Terminal

run the following commands in your terminal:

git clone https://github.com/YonghaoZhao722/YeastSAM.git
cd YeastSAM
conda env create -f environment.yml
conda activate yeastsam
pip install .

With Installer

YeastSAM can also be installed using the installer.

• MacOS: YeastSAM.pkg

Usage

Command Line Usage

To start the YeastSAM tools launcher:

conda activate yeastsam
yeastsam

This will open a GUI with four main sections:

1. Generate Masks

• napari: Opens napari viewer for interactive mask generation and editing. You can load our custom weight YeastSAM for better accuracy in budding yeast.

2. Optional Tools

This section is to fix the offset between smFISH image and DIC (as masks are generated from DIC).

• Shift Analyzer: Analyze and detect shifts in your image data
• Apply Registration: Apply image registration corrections

3. Convert to Outline File

• Mask2Outline: Convert mask files to FISH-Quant compatible outline format

4. Separation Module

• Mask Editor: You can annotate mask images manually or with CNN & U-Net separation module. Download models at GitHub Releases.

Tools Overview

The tools/ directory contains the following utilities:

• shift.py: Shift detection and analysis
• registration.py: Image registration functionality
• Mask2Outline.py: Mask to outline conversion
• mask_editor.py: Advanced mask editing interface

Citation

If you are using YeastSAM or the dataset for your research, please cite our paper:

Zhao, Y., Zhu, Z., Yang, S., Li, W. YeastSAM: A Deep Learning Model for Accurate Segmentation of Budding Yeast Cells. bioRxiv 2025.09.17.676679 (2025). https://doi.org/10.1101/2025.09.17.676679

Acknowledgement

We acknowledge the following reference for inspiring our work:

Archit, A., Freckmann, L., Nair, S. et al. Segment Anything for Microscopy. Nat Methods 22, 579–591 (2025). https://doi.org/10.1038/s41592-024-02580-4

See the full COPYRIGHT notice for details.