  Image-guided mouse irradiation is essential to understand interventions
involving radiation prior to human studies. Our objective is to employ Swin
UNEt Transformers (Swin UNETR) to segment native micro-CT and contrast-enhanced
micro-CT scans and benchmark the results against 3D no-new-Net (nnU-Net). Swin
UNETR reformulates mouse organ segmentation as a sequence-to-sequence
prediction task, using a hierarchical Swin Transformer encoder to extract
features at 5 resolution levels, and connects to a Fully Convolutional Neural
Network (FCNN)-based decoder via skip connections. The models were trained and
evaluated on open datasets, with data separation based on individual mice.
Further evaluation on an external mouse dataset acquired on a different
micro-CT with lower kVp and higher imaging noise was also employed to assess
model robustness and generalizability. Results indicate that Swin UNETR
consistently outperforms nnU-Net and AIMOS in terms of average dice similarity
coefficient (DSC) and Hausdorff distance (HD95p), except in two mice of
intestine contouring. This superior performance is especially evident in the
external dataset, confirming the model's robustness to variations in imaging
conditions, including noise and quality, thereby positioning Swin UNETR as a
highly generalizable and efficient tool for automated contouring in
pre-clinical workflows.
