Automatic segmentation of the interscapular brown adipose tissue in rats based on deep learning using the dynamic magnetic resonance fat fraction images.

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Chuanli Cheng, Bingxia Wu, Lei Zhang, Qian Wan, Hao Peng, Xin Liu, Hairong Zheng, Huimao Zhang, Chao Zou
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引用次数: 0

Abstract

Objective: 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.

Materials and methods: 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).

Result: 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.

Conclusion: An automatic iBAT segmentation method for rats was successfully developed using the dynamic labelled FF images of activated BAT and deep learning network.

Abstract Image

基于深度学习的大鼠肩胛间棕色脂肪动态磁共振图像自动分割。
目的:利用去甲肾上腺素(NE)刺激下的动态MR脂肪部分(FF)图像,建立大鼠肩胛间BAT (iBAT)的准确标记方法,并建立基于U-Net模型的iBAT自动分割方法。材料和方法:34只大鼠分别饲喂不同的饲料或饲养在不同的温度下,在注射NE前后进行连续磁共振扫描。iBAT通过识别在NE刺激下FF变化明显的区域来自动标记。此外,这些FF图像与识别的iBAT掩模图像一起用于开发深度学习网络,以在各种大鼠模型中实现iBAT的鲁棒分割,即使没有NE刺激。然后在高脂饲料(HFD)和正常饲料(ND)喂养的大鼠中验证训练模型。结果:临床3.0 T MR扫描共采集FF图像6510张。自动标记结果与手工标记结果的骰子相似系数(DSC)为0.895±0.022。对于网络训练,DSC、准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iBAT体积和FF值均高于ND大鼠,NE注射后ND大鼠iBAT体积和FF值下降幅度更大。结论:利用激活BAT的动态标记FF图像和深度学习网络,成功建立了大鼠iBAT自动分割方法。
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来源期刊
CiteScore
4.60
自引率
0.00%
发文量
58
审稿时长
>12 weeks
期刊介绍: MAGMA is a multidisciplinary international journal devoted to the publication of articles on all aspects of magnetic resonance techniques and their applications in medicine and biology. MAGMA currently publishes research papers, reviews, letters to the editor, and commentaries, six times a year. The subject areas covered by MAGMA include: advances in materials, hardware and software in magnetic resonance technology, new developments and results in research and practical applications of magnetic resonance imaging and spectroscopy related to biology and medicine, study of animal models and intact cells using magnetic resonance, reports of clinical trials on humans and clinical validation of magnetic resonance protocols.
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