Deep learning-based Segmentation of Rabbit fetal skull with limited and
  sub-optimal annotations
Rajath Soans Alexa Gleason Tosha Shah Corey Miller Barbara Robinson Kimberly Brannen Antong Chen
In this paper, we propose a deep learning-based method to segment the
skeletal structures in the micro-CT images of Dutch-Belted rabbit fetuses which
can assist in the assessment of drug-induced skeletal abnormalities as a
required study in developmental and reproductive toxicology (DART). Our
strategy leverages sub-optimal segmentation labels of 22 skull bones from 26
micro-CT volumes and maps them to 250 unlabeled volumes on which a deep
CNN-based segmentation model is trained. In the experiments, our model was able
to achieve an average Dice Similarity Coefficient (DSC) of 0.89 across all
bones on the testing set, and 14 out of the 26 skull bones reached average DSC
>0.93. Our next steps are segmenting the whole body followed by developing a
model to classify abnormalities.