# vivoBodySeg: Machine learning-based analysis of C. elegans immobilized in vivoChip for automated developmental toxicity testing

## TL;DR

This paper introduces an automated system using machine learning to analyze C. elegans in microfluidic devices for faster and more accurate developmental toxicity testing.

## Contribution

The novel contribution is a 2.5D U-Net model (vivoBodySeg) for precise segmentation of C. elegans in vivoChip images, enabling high-throughput DevTox studies.

## Key findings

- vivoBodySeg achieves an average Dice score of 97.80 for C. elegans segmentation.
- The platform processes 36 GB of data in 35 minutes, 140x faster than manual analysis.
- Highly reproducible DevTox parameters (4–8% CV) enable accurate toxicity assessments.

## Abstract

Developmental toxicity (DevTox) tests evaluate the adverse effects of chemical exposures on an organism’s development. While large animal tests are currently heavily relied on, the development of new approach methodologies (NAMs) is encouraging industries and regulatory agencies to evaluate these novel assays. Several practical advantages have made C. elegansa useful model for rapid toxicity testing and studying developmental biology. Although the potential to study DevTox is promising, current low-resolution and labor-intensive methodologies prohibit the use of C. elegans for sub-lethal DevTox studies at high throughputs. With the recent availability of a large-scale microfluidic device, vivoChip, we can now rapidly collect 3D high-resolution images of ~ 1,000 C. elegans from 24 different populations. In this paper, we demonstrate DevTox studies using a 2.5D U-Net architecture (vivoBodySeg) that can precisely segment C. elegans in images obtained from vivoChip devices, achieving an average Dice score of 97.80. The fully automated platform can analyze 36 GB data from each device to phenotype multiple body parameters within 35 min on a desktop PC at speeds ~ 140x faster than the manual analysis. Highly reproducible DevTox parameters (4–8% CV) and additional autofluorescence-based phenotypes allow us to assess the toxicity of chemicals with high statistical power.