Chinese Journal of Magnetic Resonance >

2025 , Vol. 42 >Issue 4: 378 - 389

DOI: https://doi.org/10.11938/cjmr20253155

Articles

Study on Pancreas Automatic Segmentation, Regional Quantification, and Diabetes Assessment

  • LI Yinghao ,
  • WANG Lihui ,
  • WANG Sucheng ,
  • ZHU Zhongqi ,
  • HUANG Changdong ,
  • LI Renfeng ,
  • CAO Kaiming ,
  • HU Haiyang ,
  • JIA Yiming ,
  • LIANG Songtao ,
  • YANG Guang ,
  • LU Qing ,
  • WANG Hongzhi
Expand
  • 1. Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai 200062, China
    2. Institute of Magnetic Resonance and Molecular Imaging in Medicine, East China Normal University, Shanghai 200062, China
    3. Department of Radiology, Shanghai East Hospital, Tongji University, Shanghai 200123, China

Received date: 2025-03-27

  Online published: 2025-04-23

Fold

Abstract

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.

Key words： magnetic resonance imaging (MRI); type 2 diabetes mellitus (T2DM) prediction; deep learning; pancreas; nnU-Net

Cite this article

LI Yinghao , WANG Lihui , WANG Sucheng , ZHU Zhongqi , HUANG Changdong , LI Renfeng , CAO Kaiming , HU Haiyang , JIA Yiming , LIANG Songtao , YANG Guang , LU Qing , WANG Hongzhi . Study on Pancreas Automatic Segmentation, Regional Quantification, and Diabetes Assessment[J]. Chinese Journal of Magnetic Resonance, 2025 , 42(4) : 378 -389 . DOI: 10.11938/cjmr20253155

References

[1] ONG K L, STAFFORD L K, MCLAUGHLIN S A, et al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021[J]. Lancet, 2023, 402(10397): 203-234.
[2] TURNER C D, BAGNARA J T. General endocrinology[M]. 6th ed. Philadelphia: W. B. Saunders Company, 1976.
[3] TAYLOR R. Understanding the cause of type 2 diabetes[J]. Lancet Diabetes Endocrinol, 2024, 12(9): 664-673.
[4] WANG X, MISAWA R, ZIELINSKI M C, et al. Regional differences in islet distribution in the human pancreas-preferential beta-cell loss in the head region in patients with type 2 diabetes[J]. PloS one, 2013, 8(6): e67454.
[5] SARMA M K, SAUCEDO A, DARWIN C H, et al. Noninvasive assessment of abdominal adipose tissues and quantification of hepatic and pancreatic fat fractions in type 2 diabetes mellitus[J]. Magn Reson Imaging, 2020, 72: 95-102.
[6] NADARAJAH C, FANANAPAZIR G, CUI E, et al. Association of pancreatic fat content with type II diabetes mellitus[J]. Clin Radiol, 2020, 75(1): 51-56.
[7] GO VLW, DIMAGNO E P, GARDNER J D, et al. The Pancreas: An Integrated Textbook of Basic Science, Medicine, and Surgery[M]. 2nd ed. Hoboken: Blackwell Publishing, 2004.
[8] SHEN J, BAUM T, CORDES C, et al. Automatic segmentation of abdominal organs and adipose tissue compartments in water-fat MRI: application to weight-loss in obesity[J]. Eur J Radiol, 2016, 85(9): 1613-1621.
[9] WOLZ R, CHU C, MISAWA K, et al. Automated abdominal multi-organ segmentation with subject-specific atlas generation[J]. IEEE Trans Med Imaging, 2013, 32(9): 1723-1730.
[10] CHU C, ODA M, KITASAKAI T, et al. Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images[C]// Medical Image Computing and Computer-Assisted Intervention-MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II 16. Springer Berlin Heidelberg, 2013: 165-172.
[11] SAITO A, NAWANO S, SHIMIZU A. Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs[J]. Med Image Anal, 2016, 28: 46-65.
[12] 王鑫. 基于统计模型的胰腺分割算法的研究与实现[D]. 沈阳: 东北大学, 2013.
[13] DAI J L, HE C, WU J, et al. Pancreatic cystic neoplasms segmentation network combining dual decoding and global attention upsampling modules[J]. Chinese J Magn Reson, 2024, 41(2): 151-161.
  戴俊龙, 何聪, 武杰, 等. 融合双解码和全局注意力上采样模块的胰腺囊性肿瘤分割网络[J]. 波谱学杂志, 2024, 41(2): 151-161.
[14] CHEN L, WAN L. CTUNet: automatic pancreas segmentation using a channel-wise transformer and 3D U-Net[J]. Vis Comput, 2023, 39(11): 5229-5243.
[15] PAITHANE P, KAKARWA S. LMNS-Net: Lightweight multiscale novel semantic-net deep learning approach used for automatic pancreas image segmentation in CT scan images[J]. Expert Syst Appl, 2023, 234: 121064.
[16] GHORPADE H, JAGTA J, PATIL S, et al. Automatic segmentation of pancreas and pancreatic tumor: a review of a decade of research[J]. IEEE Access, 2023, 11: 108727-108745.
[17] ISENSEE F, JAEGER P F, KOHL S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nat Methods, 2021, 18(2): 203-211.
[18] SULOCHANA S, SIVAKAMI T. A gross morphological study of the pancreas in human cadavers[J]. Natl J Clin Anat, 2012, 1(2): 55-60.
[19] KUKU G M, HITTATIYA K, SPRINKART A M, et al. Comparison between modified Dixon MRI techniques, MR spectroscopic relaxometry, and different histologic quantification methods in the assessment of hepatic steatosis[J]. Eur Radiol, 2015, 25: 2869-2879.
[20] FEDOROV A, BEICHEL R, KALPATHY-CRAMER J, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network[J]. Magn Reson Imaging, 2012, 30(9): 1323-1341.
[21] BI X L, LU M, XIAO B, et al. Pancreas segmentation based on dual-decoder U-Net convolutional neural network[J]. J Softw, 2022, 33(5): 1947-1958.
  毕秀丽, 陆猛, 肖斌, 等. 基于双解码U型卷积神经网络的胰腺分割[J]. 软件学报, 2022, 33(5): 1947-1958.
[22] ZHANG Z, KELES E, DURAK G, et al. Large-scale multi-center CT and MRI segmentation of pancreas with deep learning[J]. Med Image Anal, 2025, 99: 103382.
[23] QU T, LI X, WANG X, et al. Transformer guided progressive fusion network for 3D pancreas and pancreatic mass segmentation[J]. Med Image Anal, 2023, 86: 102801.
[24] CHEN J, CHEN W, ZHU Z, et al. MAFE-Net: A multi-level attention feature extraction network for pancreas segmentation[J]. J Mach Learn, 2024,10: 1-19.
[25] SKUDDE-HILL L, SEQUEIRA I R, CHO J, et al. Fat distribution within the pancreas according to diabetes status and insulin traits[J]. Diabetes, 2022, 71(6): 1182-1192.
Options
Outlines

/

〈 〉

Copyright © 2017 Editorial Board of Chinese Journal of Magnetic Resonance
Powered by Beijing Magtech Co. Ltd