Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, China.
Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China.
MAGMA. 2024 Apr;37(2):215-226. doi: 10.1007/s10334-023-01133-8. Epub 2023 Nov 29.
The study aims to propose an accurate labelling method of interscapular BAT (iBAT) in rats using dynamic MR fat fraction (FF) images with noradrenaline (NE) stimulation and then develop an automatic iBAT segmentation method using a U-Net model.
Thirty-four rats fed different diets or housed at different temperatures underwent successive MR scans before and after NE injection. The iBAT were labelled automatically by identifying the regions with obvious FF change in response to the NE stimulation. Further, these FF images along with the recognized iBAT mask images were used to develop a deep learning network to accomplish the robust segmentation of iBAT in various rat models, even without NE stimulation. The trained model was then validated in rats fed with high-fat diet (HFD) in comparison with normal diet (ND).
A total of 6510 FF images were collected using a clinical 3.0 T MR scanner. The dice similarity coefficient (DSC) between the automatic and manual labelled results was 0.895 ± 0.022. For the network training, the DSC, precision rate, and recall rate were found to be 0.897 ± 0.061, 0.901 ± 0.068 and 0.899 ± 0.086, respectively. The volumes and FF values of iBAT in HFD rats were higher than ND rats, while the FF decrease was larger in ND rats after NE injection.
An automatic iBAT segmentation method for rats was successfully developed using the dynamic labelled FF images of activated BAT and deep learning network.
本研究旨在提出一种利用去甲肾上腺素（NE）刺激下的动态磁共振脂肪分数（FF）图像对大鼠肩胛间棕色脂肪（iBAT）进行准确标记的方法，然后利用 U-Net 模型开发一种自动 iBAT 分割方法。
34 只给予不同饮食或处于不同温度环境下的大鼠在注射 NE 前后进行连续磁共振扫描。通过识别对 NE 刺激有明显 FF 变化的区域，自动标记 iBAT。此外，还使用这些 FF 图像以及识别的 iBAT 掩模图像来开发一种深度学习网络，以实现各种大鼠模型中 iBAT 的稳健分割，即使没有 NE 刺激也是如此。然后，在给予高脂肪饮食（HFD）的大鼠与给予正常饮食（ND）的大鼠中验证训练好的模型。
使用临床 3.0T MR 扫描仪共采集了 6510 张 FF 图像。自动和手动标记结果之间的 Dice 相似系数（DSC）为 0.895±0.022。对于网络训练，DSC、精确率和召回率分别为 0.897±0.061、0.901±0.068 和 0.899±0.086。HFD 大鼠的 iBAT 体积和 FF 值高于 ND 大鼠，而注射 NE 后 ND 大鼠的 FF 下降幅度更大。
利用激活的 BAT 的动态标记 FF 图像和深度学习网络，成功开发了一种用于大鼠的自动 iBAT 分割方法。
