Evidence map›Paper›PMID 38957379›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2023

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

Yusuf Akhtar, Jayaram K Udupa, Yubing Tong, Tiange Liu, Caiyun Wu, Dewey Odhner, Joseph M Mcdonough, Carina Lott, Abbie Clark, Jason B Anari and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2023. 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. Article
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

12 authors.

Yusuf AkhtarMedical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Jayaram K UdupaMedical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Yubing TongMedical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Tiange LiuSchool of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
Caiyun WuMedical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Dewey OdhnerMedical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Joseph M McdonoughThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, USA.
Carina LottThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, USA.
Abbie ClarkThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, USA.
Jason B AnariThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, USA.
Patrick CahillThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, USA.
Drew A TorigianMedical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, 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
NHLBI NIH HHS R01 HL150147
6 · The paper itself

Abstract

Quantitative analysis of the dynamic properties of thoraco-abdominal organs such as lungs during respiration could lead to more accurate surgical planning for disorders such as Thoracic Insufficiency Syndrome (TIS). This analysis can be done from semi-automatic delineations of the aforesaid organs in scans of the thoraco-abdominal body region. Dynamic magnetic resonance imaging (dMRI) is a practical and preferred imaging modality for this application, although automatic segmentation of the organs in these images is very challenging. In this paper, we describe an auto-segmentation system we built and evaluated based on dMRI acquisitions from 95 healthy subjects. For the three recognition approaches, the system achieves a best average location error (LE) of about 1 voxel for the lungs. The standard deviation (SD) of LE is about 1-2 voxels. For the delineation approach, the average Dice coefficient (DC) is about 0.95 for the lungs. The standard deviation of DC is about 0.01 to 0.02 for the lungs. The system seems to be able to cope with the challenges posed by low resolution, motion blur, inadequate contrast, and image intensity non-standardness quite well. We are in the process of testing its effectiveness on TIS patient dMRI data and on other thoraco-abdominal organs including liver, kidneys, and spleen.

Indexed as

artificial intelligenceauto-segmentationdeep neural networksdynamic magnetic resonance imaging (dMRI)Lungnatural intelligencepediatric subjectsThoracic Insufficiency Syndrome

Identifiers

PMID38957379
PMCPMC11218912

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.