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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

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Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets.

Dennis Eschweiler¹, Rüveyda Yilmaz¹, Matisse Baumann¹

¹RWTH Aachen University, Institute of Imaging and Computer Vision, Aachen, Germany.

Plos Computational Biology

|February 20, 2024

Summary

This summary is machine-generated.

Diffusion models generate annotated microscopy images from sketches, reducing manual annotation needs for deep learning segmentation. This approach trains accurate models efficiently, even with limited synthetic data.

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Area of Science:

Computer Vision
Machine Learning
Microscopy Imaging

Background:

Deep learning for image segmentation requires large, manually annotated datasets.
Manual annotation is time-consuming, costly, and prone to errors.
Realistic image data generation is crucial for training robust models.

Purpose of the Study:

To demonstrate the efficacy of diffusion models in generating fully-annotated microscopy image datasets.
To reduce the reliance on manual annotations for training deep learning segmentation models.
To enable segmentation of diverse datasets without human input.

Main Methods:

Utilizing denoising diffusion probabilistic models for image generation.
Employing rough sketches as an unsupervised and intuitive starting point for annotation.
Training segmentation models with synthetically generated, fully-annotated data.

Main Results:

Diffusion models successfully generated realistic, annotated microscopy image datasets from sketches.
Segmentation models trained on small synthetic datasets achieved accuracy comparable to models trained on large manual datasets.
The proposed pipeline significantly reduced the need for human annotations.

Conclusions:

Diffusion models offer an effective solution for generating annotated microscopy data.
This approach streamlines the training of deep learning segmentation models.
It enables specialized and efficient segmentation across diverse datasets without manual annotation.