MitoVis: A Visually-guided Interactive Intelligent System for Neuronal Mitochondria Analysis

TL;DR

MitoVis is an interactive visualization system that enables neuroscientists to efficiently analyze neuronal mitochondria morphology by fine-tuning deep learning models and correcting errors with minimal effort, significantly speeding up analysis.

Contribution

The paper introduces MitoVis, a novel system that allows domain experts to fine-tune deep learning models and correct errors interactively without machine learning expertise.

Findings

Up to 15x faster analysis compared to manual methods

Maintains similar accuracy to manual analysis

Facilitates easy error correction and model fine-tuning

Abstract

Neurons have a polarized structure, including dendrites and axons, and compartment-specific functions can be affected by dwelling mitochondria. It is known that the morphology of mitochondria is closely related to the functions of neurons and neurodegenerative diseases. Even though several deep learning methods have been developed to automatically analyze the morphology of mitochondria, the application of existing methods to actual analysis still encounters several difficulties. Since the performance of pre-trained deep learning model may vary depending on the target data, re-training of the model is often required. Besides, even though deep learning has shown superior performance under a constrained setup, there are always errors that need to be corrected by humans in real analysis. To address these issues, we introduce MitoVis, a novel visualization system for end-to-end data processing and interactive analysis of the morphology of neuronal mitochondria. MitoVis enables interactive fine-tuning of a pre-trained neural network model without the domain knowledge of machine learning, which allows neuroscientists to easily leverage deep learning in their research. MitoVis also provides novel visual guides and interactive proofreading functions so that the users can quickly identify and correct errors in the result with minimal effort. We demonstrate the usefulness and efficacy of the system via a case study conducted by a neuroscientist on a real analysis scenario. The result shows that MitoVis allows up to 15x faster analysis with similar accuracy compared to the fully manual analysis method.