ConUNETR: A Conditional Transformer Network for 3D Micro-CT Embryonic
  Cartilage Segmentation
Nishchal Sapkota Yejia Zhang Susan M. Motch Perrine Yuhan Hsi Sirui Li Meng Wu Greg Holmes Abdul R. Abdulai Ethylin W. Jabs Joan T. Richtsmeier Danny Z Chen
Studying the morphological development of cartilaginous and osseous
structures is critical to the early detection of life-threatening skeletal
dysmorphology. Embryonic cartilage undergoes rapid structural changes within
hours, introducing biological variations and morphological shifts that limit
the generalization of deep learning-based segmentation models that infer across
multiple embryonic age groups. Obtaining individual models for each age group
is expensive and less effective, while direct transfer (predicting an age
unseen during training) suffers a potential performance drop due to
morphological shifts. We propose a novel Transformer-based segmentation model
with improved biological priors that better distills morphologically diverse
information through conditional mechanisms. This enables a single model to
accurately predict cartilage across multiple age groups. Experiments on the
mice cartilage dataset show the superiority of our new model compared to other
competitive segmentation models. Additional studies on a separate mice
cartilage dataset with a distinct mutation show that our model generalizes well
and effectively captures age-based cartilage morphology patterns.