Deep learning-based Segmentation of Rabbit fetal skull with limited and sub-optimal annotations
This work addresses a domain-specific need in developmental and reproductive toxicology for automated segmentation to assist in drug safety studies, but it is incremental as it applies existing deep learning methods to a new dataset with limited annotations.
The paper tackled the problem of segmenting skeletal structures in micro-CT images of rabbit fetuses to assess drug-induced abnormalities, achieving an average Dice Similarity Coefficient of 0.89 across all bones, with 14 bones reaching over 0.93.
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.