SAM for Bioimage Analysis: A Comprehensive Guide

Image segmentation has always been a cornerstone of image analysis, and in bioimage analysis, its significance cannot be overstated. Precise segmentation of regions of interest is crucial for downstream analysis, yet the inherent complexity of biological data makes this task particularly challenging.

While numerous segmentation methods exist, the field has long sought a generalist model capable of handling diverse applications. In April 2023, Meta introduced the Segment Anything Model (SAM)—a state-of-the-art segmentation framework trained on an unprecedented dataset of 11 million images and 1.1 billion masks. Although SAM was originally designed for natural images, its remarkable potential has captured the attention of the bioimage community, inspiring adaptations such as MicroSAM and CellSAM.

Despite these adaptations, we found the original SAM model to be impressively effective in bioimage analysis. Recognizing its capabilities, developers have built SAM plugins for popular bioimage analysis tools like Fiji and QuPath for annotation. However, Python remains the most robust way of working with SAM, offering more control and flexibility.

Sharing Our Experience: SAM Workshop Highlights

Following the success of our SAM workshop at Harvard Medical School, we were excited to extend the experience during the I2K conference in Milan this year. The positive reception at both events motivated us to share these resources with the broader community.

We are excited to make our course materials available on GitHub, where you’ll find slides, detailed notes, a course template, and code examples. Here’s what you can expect from the tutorial:

• Segment Everything with SAM
  • Discover how tweaking input parameters can significantly enhance segmentation quality.
  • Learn how to use SAM to segment entire images efficiently.

• Utilizing Prompts (Boxes and Points)
  • Explore how SAM responds to prompts like bounding boxes and point annotations, making segmentation more intuitive and flexible.

Additionally, we have provided.yaml configuration file for setting up SAM on Google Colab or a local machine, whether you’re using a Mac or an NVIDIA GPU-powered computer.

Why Share This Tutorial?

Our goal is to provide a detailed, step-by-step, and easy to follow guide to help the bioimage analysts with some Python experience understanding of a powerful, yet complex model, and highlight its potential. Whether you’re analyzing microscopy images or other biological datasets, SAM can be useful—but the initial learning curve can feel intimidating.

Through these materials, we aim to make advanced image segmentation accessible to all. We encourage you to explore the resources on our GitHub repository, experiment with SAM, and find it useful for your specific workflows.

If you have questions or would like to share your experiences, please don’t hesitate to reach out—we’d love to hear from you.

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