BackgroundQuantitative 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.PurposeTo develop and validate an automated method for pancreas segmentation into head, body, and tail subregions in abdominal MRI.Study TypeRetrospective.SubjectsOne 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/SequenceA 1.5 T, three‐dimensional two‐point Dixon sequence (for segmentation and volume assessment) and atwo‐dimensionalaxial multiecho gradient‐recalled echo sequence.AssessmentPancreas 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.Statistical TestWilcoxon signed rank test and Mann–WhitneyU‐test for comparisons. DSC and volume differences for evaluation. APvalue < 0.05 was considered statistically significant.ResultsGood 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.Data ConclusionThe developed segmentation's performance was no different from manual annotations. Application on type 2 diabetes subjects showed potential for assessing pancreatic disease heterogeneity.Level of Evidence4Technical Efficacy Stage3