Study on Pancreas Automatic Segmentation, Regional Quantification, and Diabetes Assessment
Abstrak
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.
Topik & Kata Kunci
Penulis (13)
LI Yinghao
WANG Lihui
WANG Sucheng
ZHU Zhongqi
HUANG Changdong
Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai 200062, China
CAO Kaiming
HU Haiyang
JIA Yiming
LIANG Songtao
YANG Guang
LU Qing
WANG Hongzhi
Akses Cepat
- Tahun Terbit
- 2025
- Sumber Database
- DOAJ
- DOI
- 10.11938/cjmr20253155
- Akses
- Open Access ✓