Atlas-based automated detection of swim bladder in Medaka embryo

TL;DR

This paper introduces an automated image analysis method using atlas-based segmentation and machine learning to detect swim bladders in Medaka fish embryos, aiding toxicity and efficacy assessments.

Contribution

It presents a novel automated approach combining atlas-based segmentation and random forest classification for swim bladder detection in embryo images.

Findings

Achieved 95% average precision rate on a dataset of 261 images.

Successfully distinguished embryos with and without swim bladders.

Effective in both dorsal and lateral views.

Abstract

Fish embryo models are increasingly being used both for the assessment of chemicals efficacy and potential toxicity. This article proposes a methodology to automatically detect the swim bladder on 2D images of Medaka fish embryos seen either in dorsal view or in lateral view. After embryo segmentation and for each studied orientation, the method builds an atlas of a healthy embryo. This atlas is then used to define the region of interest and to guide the swim bladder segmentation with a discrete globally optimal active contour. Descriptors are subsequently designed from this segmentation. An automated random forest clas-sifier is built from these descriptors in order to classify embryos with and without a swim bladder. The proposed method is assessed on a dataset of 261 images, containing 202 embryos with a swim bladder (where 196 are in dorsal view and 6 are in lateral view) and 59 without (where 43 are in dorsal view and 16 are in lateral view). We obtain an average precision rate of 95% in the total dataset following 5-fold cross-validation.