胰腺自动分割与区域定量及糖尿病评估研究
收稿日期: 2025-03-27
网络出版日期: 2025-04-23
基金资助
上海市卫生健康委员会卫生行业临床研究专项(202340212);上海市东方医院引进人才项目(DFRC2023015)
Study on Pancreas Automatic Segmentation, Regional Quantification, and Diabetes Assessment
Received date: 2025-03-27
Online published: 2025-04-23
胰腺健康与糖尿病等疾病密切相关，准确检测胰腺脂肪含量对疾病的早期诊断和干预具有重要意义．本文提出了一种基于深度学习的胰腺自动分割与脂肪定量方法．首先，使用nnU-Net训练分割模型，实现对m-Dixon序列中胰腺整体的高精度分割，测试集DSC系数（Dice Similarity Coefficient，DSC）达0.92．随后，提出一种自动分区与脂肪定量评估方法，实现胰腺头、体、尾的精准划分，并定量分析其体积及脂肪含量．基于256例受试者的研究结果表明，胰腺尾部脂肪含量与2型糖尿病显著相关（p < 0.05）．进一步利用随机森林分类模型进行糖尿病风险预测，其中基于尾部脂肪含量的分类曲线下面积（Area Under the Curve，AUC）为0.68，而结合多区域脂肪信息构建的组合脂肪含量的分类AUC达0.73．研究结果表明，该方法可为糖尿病的早期诊断提供有效的技术支持.
李英豪 , 王丽辉 , 王苏成 , 朱中旗 , 黄长栋 , 李仁峰 , 曹开明 , 胡海洋 , 贾一鸣 , 梁松涛 , 杨光 , 路青 , 汪红志 . 胰腺自动分割与区域定量及糖尿病评估研究[J]. 波谱学杂志, 2025 , 42(4) : 378 -389 . DOI: 10.11938/cjmr20253155
Pancreatic health is closely linked to diabetes, making accurate fat quantification crucial for early diagnosis. This study proposes a deep learning-based method for automatic pancreatic segmentation and fat quantification. The nnU-Net model achieves high-precision segmentation on m-Dixon Imaging, with a Dice similarity coefficient (DSC) of 0.92. A novel sub-region partitioning and quantification method enables precise delineation of the pancreatic head, body, and tail. Analysis of 256 subjects (healthy, prediabetic, diabetic) reveals a significant association between pancreatic tail fat and type 2 diabetes (p < 0.05). Using random forest classifiers, diabetes risk was effectively predicted based on tail fat content and a composite fat index, yielding an area under the curve (AUC) of 0.68 and 0.73, respectively. This method offers a promising tool for the early diagnosis of diabetes.
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