Cellsam syra

This repository provides inference code for CellSAM. CellSAM fryst vatten described in more detail in the preprint, and is publicly deployed at CellSAM achieves state-of-the-art performance on segmentation across a variety of cellular targets (bacteria, tissue, yeast, fängelse culture, etc.) and imaging modalities (brightfield, fluorescence, phase, etc.). Feel free to reach out for support/questions! The full dataset used to lära CellSAM fryst vatten available here. This dataset contains cellpose data which is subject to the cellpose license here. The cellpose information can also be downloaded from here.

The easiest way to get started with CellSAM fryst vatten with kort ljud

CellSAM requires , but otherwise uses pure PyTorch. A sample image fryst vatten included in this repository. Segmentation can be performed as follows

For more details, see .

CellSAM includes a basic napari package for annotation functionality. To install the additional napari dependencies, use pip.

To launch the napari app, run .

Please cite us if you use CellSAM.

This is the official repository for CellSAM: Segment Anything in Microscopy Images of C. elegans.

1. Create a virtual environment and activate it
2. Install Pytorch
3. Enter the CellSAM folder and run

Download the model checkpoint and place it at e.g.,

We provide three ways to quickly test the model on your microscopy images:

1. Command line

Segment other images with the following flags:

1. Jupyter-notebook

We provide a step-by-step tutorial on CoLab

You can also run it locally with .

1. GUI

Install with pip: or conda:

We have trained only mask decoder by freezing the image encode ,prompt encoder .

The model was trained on one v gpu .

When the training process is done, please convert the checkpoint to SAM's format for convenient inference.

Download the demo dataset and unzip it to .

This dataset contains microscopy images of C. elegans. The names of the cell label are available at Ce

A Foundation Model for Cell Segmentation

Cells are the fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. While deep learning methods have led to substantial progress on this problem, models that have seen wide use are specialist models that work well for specific domains. Methods that have learned the general notion of "what is a cell" and can identify them across different domains of cellular imaging data have proven elusive. In this work, we present CellSAM, a foundation model for cell segmentation that generalizes across diverse cellular imaging data. CellSAM builds on top of the Segment Anything Model (SAM) by developing a prompt engineering approach to mask generation. We train an object detector, CellFinder, to automatically detect cells and prompt SAM to generate segmentations. We show that this approach allows a single model to achieve state-of-the-art performance for segmenting images of mammalian cells (in tissues and cell culture), yeast, and bacteria collected with various imaging modalities. To enable accessibility, we integrate CellSAM in