How to install micro-sam for use with napari?

Install via conda from the conda-forge channel using 'conda install -c conda-forge micro_sam', which handles dependencies. For GPU acceleration, ensure CUDA-compatible drivers are set up as per the documentation.

Micro-SAM vs napari-sam: which is better for 3D microscopy?

Micro-SAM is superior for 3D microscopy because it includes fine-tuned models specifically for volumetric data like electron microscopy, whereas napari-sam has more basic 3D support without domain optimization.

Can I run micro-sam without a GPU?

Yes, but performance will be slower, especially for 3D segmentation and tracking. The documentation notes that CPU inference is possible but may not be practical for large datasets or interactive use.

What microscopy image types does micro-sam work best with?

It's optimized for common biological microscopy modalities like fluorescence imaging for cells and electron microscopy for organelles, with fine-tuned models that adapt to specific structures mentioned in the examples.

How to fine-tune micro-sam for custom microscopy data?

Refer to the project's documentation and related tools like peft-sam for guidance. The codebase includes fine-tuning methods, but it requires Python expertise and familiarity with model training workflows.

Is micro-sam good for processing large batches of images automatically?

Not ideally, as it's designed for interactive use. For batch processing, you may need to script around the napari integration, which can be cumbersome compared to dedicated non-interactive tools.

Open-Awesome

MicroSAM

MITJupyter Notebookv1.8.4

Interactive segmentation and tracking tools for microscopy images built on Segment Anything.

Visit Website GitHub

693 stars105 forks0 contributors

What is MicroSAM?

Micro-SAM is an open-source toolkit that adapts the Segment Anything Model (SAM) for microscopy image analysis. It provides interactive tools for segmenting and tracking biological structures in 2D and 3D microscopy images with minimal user input. The project includes fine-tuned models optimized for microscopy data to improve accuracy and efficiency.

Target Audience

Bioimage analysts, computational biologists, and researchers working with microscopy data who need efficient, interactive segmentation and tracking tools for cells and subcellular structures.

Value Proposition

Developers choose Micro-SAM for its specialized fine-tuned models for microscopy, support for both 2D and 3D segmentation plus tracking, and seamless integration with napari for an interactive workflow, reducing manual annotation time.

Overview

Segment Anything for Microscopy

Use Cases

Best For

Interactive segmentation of cells in 2D microscopy images
Volumetric segmentation of organelles in 3D electron microscopy data
Tracking cell movements and divisions over time in live-cell imaging
Reducing manual annotation effort in bioimage analysis pipelines
Integrating foundation models into microscopy workflows via napari
Fine-tuning Segment Anything models for domain-specific microscopy tasks

Not Ideal For

Projects requiring fully automated, high-throughput batch processing without any user interaction
Real-time microscopy analysis where low-latency, instantaneous segmentation is critical
General computer vision tasks outside microscopy, such as natural image or satellite imagery segmentation

Pros & Cons

Pros

Specialized Fine-Tuned Models

Includes models specifically optimized for microscopy data, such as cells and mitochondria, which improve accuracy over the base Segment Anything Model for biological structures.

Interactive Napari Integration

Provides seamless napari plugins for point-and-click segmentation and tracking, reducing manual annotation effort with minimal user input, as highlighted in the quickstart videos.

Comprehensive Application Support

Supports 2D segmentation, 3D volumetric analysis, and 2D tracking in a single toolkit, offering more functionality than similar napari plugins like napari-sam or napari-segment-anything.

Active Development and Support

Backed by a published Nature Methods paper, detailed documentation, video tutorials, and active community support on image.sc, ensuring reliability and ongoing improvements.

Cons

GUI Dependency Limitations

Heavily reliant on the napari GUI for interactive use, making it unsuitable for headless server deployments or fully automated pipelines without significant customization.

High Computational Demands

The fine-tuned models, especially for 3D volumetric data, require substantial GPU memory and processing power, which can be a bottleneck for users with limited hardware resources.

Complex Setup and Dependencies

Installation via conda and management of Python environments can be challenging, with potential issues for users not familiar with bioimage analysis toolchains.

Frequently Asked Questions

Related Projects

Cellpose

a generalist algorithm for cellular segmentation with human-in-the-loop capabilities

Stars2,242

Forks631

Last commit7 days ago

StarDist

StarDist - Object Detection with Star-convex Shapes

Stars1,229

Forks275

Last commit4 months ago

HoVer-Net

Simultaneous Nuclear Instance Segmentation and Classification in H&E Histology Images.

Stars720

Forks272

Last commit2 years ago

Squidpy

Spatial Single Cell Analysis in Python

Stars577

Forks116

Last commit1 day ago

Community-curated · Updated weekly · 100% open source

Found a gem we're missing?

Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.

Submit a project Star on GitHub