MitoVis: A Unified Visual Analytics System for End-to-End Neuronal Mitochondria Analysis

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Keywords

Biomedical and medical visualization;intelligence analysis;machine learning;task and requirements analysis;user interfaces

Abstract

Neurons have a polarized structure, with dendrites and axons, and compartment-specific functions can be affected by the dwelling mitochondria. Recent studies have shown that the morphology of mitochondria is closely related to the functions of neurons and neurodegenerative diseases. However, the conventional mitochondria analysis workflow mainly relies on manual annotations and generic image-processing software. Moreover, even though there have been recent developments in automatic mitochondria analysis using deep learning, the application of existing methods in a daily analysis remains challenging because the performance of a pretrained deep learning model can vary depending on the target data, and there are always errors in inference time, requiring human proofreading. To address these issues, we introduce MitoVis, a novel visualization system for end-to-end data processing and an interactive analysis of the morphology of neuronal mitochondria. MitoVis introduces a novel active learning framework based on recent contrastive learning, which allows accurate fine-tuning of the neural network model. MitoVis also provides novel visual guides for interactive proofreading so that users can quickly identify and correct errors in the result with minimal effort. We demonstrate the usefulness and efficacy of the system via case studies conducted by neuroscientists. The results show that MitoVis achieved up to 13.3× faster total analysis time in the case study compared to the conventional manual analysis workflow.