Department of Engineering Science, University of Oxford, Oxford, UK.
Perspectum Ltd, Oxford, UK.
J Magn Reson Imaging. 2022 Oct;56(4):997-1008. doi: 10.1002/jmri.28098. Epub 2022 Feb 7.
Quantitative imaging studies of the pancreas have often targeted the three main anatomical segments, head, body, and tail, using manual region of interest strategies to assess geographic heterogeneity. Existing automated analyses have implemented whole-organ segmentation, providing overall quantification but failing to address spatial heterogeneity.
To develop and validate an automated method for pancreas segmentation into head, body, and tail subregions in abdominal MRI.
Retrospective.
One hundred and fifty nominally healthy subjects from UK Biobank (100 subjects for method development and 50 subjects for validation). A separate 390 UK Biobank triples of subjects including type 2 diabetes mellitus (T2DM) subjects and matched nondiabetics.
FIELD STRENGTH/SEQUENCE: A 1.5 T, three-dimensional two-point Dixon sequence (for segmentation and volume assessment) and a two-dimensional axial multiecho gradient-recalled echo sequence.
Pancreas segments were annotated by four raters on the validation cohort. Intrarater agreement and interrater agreement were reported using Dice overlap (Dice similarity coefficient [DSC]). A segmentation method based on template registration was developed and evaluated against annotations. Results on regional pancreatic fat assessment are also presented, by intersecting the three-dimensional parts segmentation with one available proton density fat fraction (PDFF) image.
Wilcoxon signed rank test and Mann-Whitney U-test for comparisons. DSC and volume differences for evaluation. A P value < 0.05 was considered statistically significant.
Good intrarater (DSC mean, head: 0.982, body: 0.940, tail: 0.961) agreement and interrater (DSC mean, head: 0.968, body: 0.905, tail: 0.943) agreement were observed. No differences (DSC, head: P = 0.4358, body: P = 0.0992, tail: P = 0.1080) were observed between the manual annotations and our method's segmentations (DSC mean, head: 0.965, body: 0.893, tail: 0.934). Pancreatic body PDFF was different between T2DM and nondiabetics matched by body mass index.
The developed segmentation's performance was no different from manual annotations. Application on type 2 diabetes subjects showed potential for assessing pancreatic disease heterogeneity.
4 TECHNICAL EFFICACY STAGE: 3.
胰腺的定量成像研究通常针对三个主要的解剖节段，即头部、体部和尾部，使用手动感兴趣区域策略来评估地理异质性。现有的自动化分析已经实现了整个器官的分割，提供了整体定量分析，但未能解决空间异质性。
开发并验证一种用于腹部 MRI 中胰腺头部、体部和尾部亚区分割的自动化方法。
回顾性研究。
来自英国生物银行的 150 名名义健康受试者（100 名用于方法开发，50 名用于验证）。另外还有 390 名来自英国生物银行的三对受试者，包括 2 型糖尿病（T2DM）患者和匹配的非糖尿病患者。
磁场强度/序列：1.5T 三维两点 Dixon 序列（用于分割和体积评估）和二维轴向多回波梯度回波序列。
在验证队列中，四名评估员对胰腺段进行了注释。报告了内部和外部评估者之间的一致性，使用 Dice 重叠（Dice 相似系数[DSC]）。开发了一种基于模板注册的分割方法，并对其进行了评估。还通过与一个可用的质子密度脂肪分数（PDFF）图像相交，展示了三维部分分割在胰腺脂肪评估中的结果。
Wilcoxon 符号秩检验和 Mann-Whitney U 检验用于比较。DSC 和体积差异评估。P 值 < 0.05 被认为具有统计学意义。
观察到良好的内部评估者（DSC 平均值，头部：0.982，体部：0.940，尾部：0.961）和外部评估者（DSC 平均值，头部：0.968，体部：0.905，尾部：0.943）之间的一致性。手动注释和我们的方法分割之间没有差异（DSC，头部：P = 0.4358，体部：P = 0.0992，尾部：P = 0.1080）（DSC 平均值，头部：0.965，体部：0.893，尾部：0.934）。通过体质指数匹配的 T2DM 和非糖尿病患者的胰腺体部 PDFF 不同。
开发的分割方法的性能与手动注释没有区别。在 2 型糖尿病患者中的应用显示了评估胰腺疾病异质性的潜力。
4 技术功效阶段：3.
