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.

通过改进的生物先验条件，我们提出了一种基于Transformer的分割模型，能够通过条件机制更好地提取形态多样的信息，从而使单个模型能够准确预测多个年龄组的软骨。实验证明，与其他竞争性分割模型相比，我们的新模型在小鼠软骨数据集上表现出更高的优越性。进一步研究表明，我们的模型在另一个具有不同突变的小鼠软骨数据集上具有良好的泛化能力，并有效捕捉基于年龄的软骨形态模式。

ConUNETR：3D微CT胚胎软骨分割的条件Transformer网络