Evidence map›Paper›PMID 38300360›Full record

ArticleMagma (New York, N.Y.)2024

Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.

Sevgi Gokce Kafali, Shu-Fu Shih, Xinzhou Li, Grace Hyun J Kim, Tristan Kelly, Shilpy Chowdhury, Spencer Loong, Jeremy Moretz, Samuel R Barnes, Zhaoping Li and 1 more

Abstract read
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In one paragraph

Article in Magma (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Sevgi Gokce KafaliDepartment of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.ORCID https://orcid.org/0000-0001-5941-5399
Shu-Fu ShihDepartment of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.
Xinzhou LiDepartment of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.
Grace Hyun J KimDepartment of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.
Tristan KellyDepartment of Physiological Science, University of California, Los Angeles, CA, USA.
Shilpy ChowdhuryDepartment of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA.
Spencer LoongDepartment of Psychology, Loma Linda University School of Behavioral Health, Loma Linda, CA, USA.
Jeremy MoretzDepartment of Neuroradiology, Loma Linda University Medical Center, Loma Linda, CA, USA.
Samuel R BarnesDepartment of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA.
Zhaoping LiDepartment of Medicine, University of California, Los Angeles, CA, USA.
Holden H WuDepartment of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA. HoldenWu@mednet.ucla.edu.ORCID http://orcid.org/0000-0002-2585-5916

Funding

Quantifying Body Composition and Liver Disease in Children using Free-Breathing MRI and MRER01DK124417 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CALKINS, KARA LYNNE, WU, HOLDEN H · 2020 to 2023
$2.3M
NIDDK NIH HHS R01 DK124417NIDDK NIH HHS R01DK124417
6 · The paper itself

Abstract

objectiveIncreased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs. MATERIALS AND

methods920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC).

resultsACD 3D U-Net achieved rapid (< 4.8 s/subject) segmentation with high DICE-SAT (median ≥ 0.994) and DICE-VAT (median ≥ 0.976), small FN (median ≤ 0.7%), and FP (median ≤ 1.1%). 3D nnU-Net yielded rapid (< 2.5 s/subject) segmentation with similar DICE-SAT (median ≥ 0.992), DICE-VAT (median ≥ 0.979), FN (median ≤ 1.1%) and FP (median ≤ 1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC > 0.997) in longitudinal analysis. DISCUSSION: ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.

Indexed as

Abdominal FatImaging, Three-DimensionalIntra-Abdominal FatMagnetic Resonance ImagingNeural Networks, ComputerObesityAdultAgedAlgorithmsContrast MediaFemaleHumansImage Interpretation, Computer-AssistedLongitudinal StudiesMaleMiddle AgedContrast MediaAdipose tissueAutomated segmentationBody compositionMagnetic resonance imagingNeural networksObesityOverweight

Identifiers

PMID38300360
PMCPMC11316694

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.