mitoclass 1.0.3

MitoClass is a napari plugin for classifying mitochondrial morphology from microscopy images: it allows preprocessing data, predicting classes (connected, fragmented, intermediate), visualizing overlays and 3D summaries, and managing a prediction history.

MitoClass

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1  Overview

MitoClass is a napari plugin for the qualitative assessment of mitochondrial network morphology. Inference is patch‑wise: each 2‑D patch—obtained from a maximum‑intensity projection of 3‑D stacks—is classified as connected, fragmented, or intermediate.

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2  Key features

Module	Description
Patch‑based inference	Analyse an image folder or the active napari layer.
RGBA heatmaps	Overlay prediction maps as semi‑transparent layers in napari.
Global statistics	Compute the proportion of pixels assigned to each morphology and identify the dominant class.
3‑D graph	Interactive Plotly scatter plot of connected / fragmented / intermediate proportions per image.

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3  Requirements

• Python ≥ 3.10
• OS : Windows, Linux or macOS
• Hardware : CPU is sufficient; GPU (CUDA 11+) is recommended for large datasets

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4  Installation

4.1  PyPI

pip install mitoclass

4.2  Reproducible conda environment

conda create -n mitoclass python=3.10
conda activate mitoclass

# (Optional) GPU acceleration
conda install -c conda-forge cudnn=8.9 cuda11.8 tensorflow

pip install mitoclass

Apple Silicon: install tensorflow-macos.

4.3  Pre‑trained model

Download the model (.h5) from <>

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5  Usage

5.1  Graphical interface

napari

1. Open Plugins → MitoClass.

2. Select the four required paths:

   Field	Purpose
   Input dir	Folder of images to analyse (.tif, .tiff, .stk, .png).
   Output dir	Destination folder for CSV and graph files.
   Heatmaps dir	Folder where heatmaps (*_map.tif) will be written.
   Model file	Pre‑trained Keras model (.h5).

3. Click Run inference. A progress bar tracks the number of processed images.

4. After completion:

   • Show heatmaps adds the newly generated *_map.tif layers to napari.
   • Show 3D graph opens graph3d.html, displaying the connected/fragmented/intermediate proportions.

Tip: Without an Input dir you may run Infer active layer; results are still saved to Output dir and Heatmaps dir.

5.2  Output structure

Folder	File(s)	Content
Output dir	predictions.csv	Pixel proportion for each class (connected, fragmented, intermediate) and the dominant morphology, one line per image.
	graph3d.html	Interactive 3‑D Plotly graph of class proportions.
Heatmaps dir	*_map.tif	One RGBA heatmap per image, ready to overlay in napari.

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6  Licence

This project is released under the GNU GPL v3 licence. See the LICENSE file for details.