  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.
