YeastSAM

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Accurate segmentation for budding yeast cells

YeastSAM 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# YeastSAM
[![MBoC](https://img.shields.io/badge/MBoC-10.1091%2Fmbc.E25--09--0454-blue)](https://doi.org/10.1091/mbc.E25-09-0454)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17204942.svg)](https://doi.org/10.5281/zenodo.17204942)

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](https://yeastsamdoc.readthedocs.io/en/latest/).

## Quick Start

If you just want to run YeastSAM, you can refer to [µSAM](https://github.com/computational-cell-analytics/micro-sam) to install the framework. We provide custom weights for better yeast cell segmentation that can be downloaded from our [GitHub Releases](https://github.com/YonghaoZhao722/YeastSAM/releases). The custom weights can be used with napari, [BAND](https://computational-cell-analytics.github.io/micro-sam/micro_sam.html#using-micro_sam-on-band), [CLI](https://computational-cell-analytics.github.io/micro-sam/micro_sam.html#using-the-command-line-interface-cli), and [QuPath](https://github.com/ksugar/qupath-extension-sam).

## 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](https://docs.conda.io/en/latest/) environment. If you do not already have Conda or Miniconda installed, please install one of them first:

- [Conda](https://docs.conda.io/projects/conda/en/stable/user-guide/install/index.html#regular-installation) or [Miniconda](https://www.anaconda.com/docs/getting-started/miniconda/install)

### With Terminal

run the following commands in your terminal:

```bash
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](https://github.com/YonghaoZhao722/YeastSAM/releases/download/1.0.1/YeastSAM.pkg)

## Usage

### Command Line Usage
To start the YeastSAM tools launcher:

```bash
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](https://github.com/YonghaoZhao722/YeastSAM/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. (2026). YeastSAM: A Deep Learning Model for Accurate Segmentation of Yeast Cells. Molecular Biology of the Cell. Advance online publication. https://doi.org/10.1091/mbc.E25-09-0454

## 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](src/COPYRIGHT) notice for details.

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