PMID- 35128748
OWN - NLM
STAT- MEDLINE
DCOM- 20220916
LR  - 20230701
IS  - 1522-2586 (Electronic)
IS  - 1053-1807 (Print)
IS  - 1053-1807 (Linking)
VI  - 56
IP  - 4
DP  - 2022 Oct
TI  - Pancreas MRI Segmentation Into Head, Body, and Tail Enables Regional Quantitative 
      Analysis of Heterogeneous Disease.
PG  - 997-1008
LID - 10.1002/jmri.28098 [doi]
AB  - BACKGROUND: 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. PURPOSE: To develop 
      and validate an automated method for pancreas segmentation into head, body, and 
      tail subregions in abdominal MRI. STUDY TYPE: Retrospective. SUBJECTS: 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. ASSESSMENT: 
      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. STATISTICAL TEST: 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. RESULTS: 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. DATA 
      CONCLUSION: The 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 EVIDENCE: 4 TECHNICAL 
      EFFICACY STAGE: 3.
CI  - (c) 2022 The Authors. Journal of Magnetic Resonance Imaging published by Wiley 
      Periodicals LLC on behalf of International Society for Magnetic Resonance in 
      Medicine.
FAU - Triay Bagur, Alexandre
AU  - Triay Bagur A
AUID- ORCID: 0000-0002-5836-737X
AD  - Department of Engineering Science, University of Oxford, Oxford, UK.
AD  - Perspectum Ltd, Oxford, UK.
FAU - Aljabar, Paul
AU  - Aljabar P
AD  - Perspectum Ltd, Oxford, UK.
FAU - Ridgway, Gerard R
AU  - Ridgway GR
AD  - Perspectum Ltd, Oxford, UK.
FAU - Brady, Michael
AU  - Brady M
AD  - Perspectum Ltd, Oxford, UK.
FAU - Bulte, Daniel P
AU  - Bulte DP
AD  - Department of Engineering Science, University of Oxford, Oxford, UK.
LA  - eng
GR  - MC_PC_17228/MRC_/Medical Research Council/United Kingdom
GR  - MC_QA137853/MRC_/Medical Research Council/United Kingdom
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220207
PL  - United States
TA  - J Magn Reson Imaging
JT  - Journal of magnetic resonance imaging : JMRI
JID - 9105850
RN  - 0 (Protons)
SB  - IM
MH  - Adipose Tissue/diagnostic imaging
MH  - *Diabetes Mellitus, Type 2/diagnostic imaging
MH  - Humans
MH  - Image Processing, Computer-Assisted/methods
MH  - Magnetic Resonance Imaging/methods
MH  - Pancreas/diagnostic imaging
MH  - Protons
MH  - Retrospective Studies
PMC - PMC10286650
OTO - NOTNLM
OT  - MRI-PDFF
OT  - NAFPD
OT  - diabetes
OT  - groupwise registration
OT  - heterogeneity
OT  - segmentation
EDAT- 2022/02/08 06:00
MHDA- 2022/09/17 06:00
PMCR- 2023/06/22
CRDT- 2022/02/07 05:40
PHST- 2022/01/19 00:00 [revised]
PHST- 2021/11/30 00:00 [received]
PHST- 2022/01/22 00:00 [accepted]
PHST- 2022/02/08 06:00 [pubmed]
PHST- 2022/09/17 06:00 [medline]
PHST- 2022/02/07 05:40 [entrez]
PHST- 2023/06/22 00:00 [pmc-release]
AID - JMRI28098 [pii]
AID - 10.1002/jmri.28098 [doi]
PST - ppublish
SO  - J Magn Reson Imaging. 2022 Oct;56(4):997-1008. doi: 10.1002/jmri.28098. Epub 2022 
      Feb 7.