Evidence map›Paper›PMID 41206361›Full record

ArticleMedical physics2025

Auto-segmentation of thoraco-abdominal organs in pediatric dynamic MRI.

Yusuf Akhtar, Jayaram K Udupa, Yubing Tong, Tiange Liu, Caiyun Wu, Rachel Kogan, Mostafa Al-Noury, Mahdie Hosseini, Leihui Tong, Samarth Mannikeri and 7 more

Abstract read
In one paragraph

Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Pediatric pancreas segmentation from MRI scans with deep learning.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Yusuf AkhtarMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Jayaram K UdupaMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Yubing TongMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Tiange LiuSchool of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China.
Caiyun WuMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Rachel KoganMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Mostafa Al-NouryMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Mahdie HosseiniMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Leihui TongMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Samarth MannikeriMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Dewey OdhnerMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Joseph M McdonoughThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Carina LottThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Abigail ClarkThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Patrick J CahillThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Jason B AnariThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Drew A TorigianMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Funding

Virtual growing child 5-dimensional functional models for treating respiratory anomaliesR01HL150147 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI TORIGIAN, DREW, UDUPA, JAYARAM K · 2020 to 2023
$3.1M
NIH HHS R01HL150147
6 · The paper itself

Abstract

purposeDynamic magnetic resonance imaging (dMRI) is a practical imaging modality for capturing information about regional thoracic-abdominal components and their dynamics in healthy children and pediatric patients with thoracic insufficiency syndrome (TIS). We propose an auto-segmentation set-up for the lungs, kidneys, liver, spleen, and thoraco-abdominal skin outer boundary (Skn) in dMRI images.

methodsThe segmentation setup has been implemented in two steps, recognition and delineation, using two deep neural network (DL) architectures, DL-R and DL-D for the recognition and delineation steps, respectively. The encoder-decoder framework in DL-D utilizes features at four different resolution levels to counter the challenges involved in segmentation. dMRI sagittal slice acquisitions of 189 (near-)normal subjects were evaluated, with an in-plane spatial resolution of roughly 1 × 1 mm

resultsAmong these three scenarios of testing, for DL-R, we achieve the best average location error (LE) of about 1 voxel for the lungs, kidneys, and spleen, and 1.5 voxels for the liver and Skn. The standard deviation (SD) of LE is about 1 or 2 voxels. For DL-D, we achieve an average Dice coefficient (DC) of about 0.92 to 0.94 for the lungs, 0.82 for the kidneys, 0.90 for the liver, 0.81 for the spleen, and 0.93 for Skn. The SD of DC is lower (0.02 to 0.07) for the lungs, liver, and Skn and slightly higher (0.06 to 0.12) for the spleen and kidneys.

conclusionsMotivated by applications in surgical planning for disorders such as TIS, adolescent idiopathic scoliosis, and early onset scoliosis, we have created an auto-segmentation system for thoraco-abdominal organs in dMRI acquisitions. This proposed setup copes with the challenges posed by low resolution, motion blur, inadequate contrast, and image intensity non-standardness in dMRI images quite well.

Indexed as

AbdomenImage Processing, Computer-AssistedMagnetic Resonance ImagingThoraxAdolescentAutomationChildChild, PreschoolHumansadolescent idiopathic scoliosisdeep neural networksdynamic MRIearly onset scoliosisimage segmentationthoracic insufficiency syndromethoraco‐abdominal organs

Identifiers

PMID41206361
PMCPMC12596233

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Registered trials

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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.