PDF(Pubmed)
Abstract：
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.
摘要：
图像引导的小鼠辐照对于在人体研究之前了解涉及辐射的干预措施至关重要。我们的目标是使用SwinUNET变压器（SwinUNETR）来分割原生微CT和对比增强微CT扫描，并根据3Dno-new-Net（nnU-Net）对结果进行基准测试。SwinUNETR将小鼠器官分割重新制定为序列到序列的预测任务，使用分层SwinTransformer编码器以5个分辨率级别提取特征，并通过跳过连接连接到基于全卷积神经网络（FCNN）的解码器。模型在开放数据集上进行训练和评估，基于个体小鼠的数据分离。还采用对在具有较低kVp和较高成像噪声的不同显微CT上获得的外部小鼠数据集的进一步评估来评估模型鲁棒性和可泛化性。结果表明，SwinUNETR在平均骰子相似系数（DSC）和Hausdorff距离（HD95p）方面始终优于nnU-Net和AIMOS，除了两只小鼠的肠道轮廓。这种卓越的性能在外部数据集中尤其明显，确认模型对成像条件变化的鲁棒性，包括噪音和质量，从而将SwinUNETR定位为临床前工作流程中自动化轮廓的高度可推广和高效的工具。
