Spotiflow¶

Spotiflow is a learning-based subpixel-accurate spot detection method for 2D and 3D fluorescence microscopy. It is primarily developed for spatial transcriptomics workflows that require transcript detection in large, multiplexed FISH-images, although it can also be used to detect spot-like structures in general fluorescence microscopy images and volumes. For more information, please refer to our paper.

Getting Started¶

Installation (pip)¶

First, create and activate a fresh conda environment (we currently support Python 3.9 to 3.13). If you don’t have conda installed, we recommend using miniforge.

$ conda create -n spotiflow python=3.12
$ conda activate spotiflow

Then, install PyTorch using pip:

$ pip install torch

Note (for Linux/Windows users with a CUDA-capable GPU): one might need to change the torch installation command depending on the CUDA version. Please refer to the PyTorch website <https://pytorch.org/get-started/locally/> for more information.

Note (for Windows users): if using Windows, if using Windows, please install the latest Build Tools for Visual Studio (make sure to select the C++ build tools during installation) before proceeding to install Spotiflow.

Finally, install spotiflow using pip:

$ pip install spotiflow

Installation (conda)¶

For Linux/MacOS users, you can also install Spotiflow using conda through the conda-forge. For directly creating a fresh environment with Spotiflow named spotiflow, you can use the following command:

$ conda create -n spotiflow -c conda-forge spotiflow python=3.12

Predicting spots in an image¶

Python API¶

The snippet below shows how to retrieve the spots from an image using one of the pretrained models:

from skimage.io import imread
from spotiflow.model import Spotiflow
from spotiflow.utils import write_coords_csv


# Load the desired image
img = imread("/path/to/your/image")

# Load a pretrained model
model = Spotiflow.from_pretrained("general")

# Predict spots
spots, details = model.predict(img) # predict expects a numpy array

# spots is a numpy array with shape (n_spots, 2)
# details contains additional information about the prediction, like the predicted heatmap, the probability per spot, the flow field, etc.

# Save the results to a CSV file
write_coords_csv(spots, "/path/to/save/spots.csv")

If a custom model needs to be used, simply change the model loading step to:

# Load a custom model
model = Spotiflow.from_folder("/path/to/model")

Command Line Interface (CLI)¶

You can use the CLI to run inference on an image or folder containing several images. To do that, you can use the following command

$ spotiflow-predict PATH

where PATH can be either point to an image file or to a folder containing different files. By default, the command will use the general pretrained model. You can specify a different model by using the --pretrained-model flag for the pre-trained models we offer or --model-dir if you want to use a custom model. After running, the detected spots are by default saved to a subfolder spotiflow_results created inside the input folder (this can be changed with the --out-dir flag). For more information, please refer to the help message of the CLI (simply run spotiflow-predict -h on your command line).

Napari plugin¶

Spotiflow also can be run easily in a graphical user interface as a napari plugin. See Predicting spots using the napari plugin for more information.