Evidence map›Paper›PMID 32648161›Full record

ArticleInternational journal of computer assisted radiology and surgery2020

Atlas-based liver segmentation for nonhuman primate research.

Jeffrey Solomon, Nina Aiosa, Dara Bradley, Marcelo A Castro, Syed Reza, Christopher Bartos, Philip Sayre, Ji Hyun Lee, Jennifer Sword, Michael R Holbrook and 4 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.1field-weighted citation impact, top 61% of its field
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

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. [EJNMMI research · 2026
    Article
  2. 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

14 authors at 3 institutions in 1 country.

Jeffrey SolomonClinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Frederick, MD, USA. jeffrey.solomon@nih.gov.ORCID http://orcid.org/0000-0003-2711-1977
Nina AiosaCenter for Infectious Disease Imaging, Clinical Center, Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, USA.
Dara BradleyCenter for Infectious Disease Imaging, Clinical Center, Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, USA.
Marcelo A CastroDivision of Clinical Research, Integrated Research Facility, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA.
Syed RezaCenter for Infectious Disease Imaging, Clinical Center, Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, USA.
Christopher BartosDivision of Clinical Research, Integrated Research Facility, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA.
Philip SayreDivision of Clinical Research, Integrated Research Facility, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA.
Ji Hyun LeeDivision of Clinical Research, Integrated Research Facility, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA.
Jennifer SwordDivision of Clinical Research, Integrated Research Facility, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA.
Michael R HolbrookDivision of Clinical Research, Integrated Research Facility, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA.
Richard S BennettDivision of Clinical Research, Integrated Research Facility, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA.
Dima A HammoudCenter for Infectious Disease Imaging, Clinical Center, Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, USA.
Reed F JohnsonDivision of Clinical Research, Integrated Research Facility, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA.
Irwin FeuersteinDivision of Clinical Research, Integrated Research Facility, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA.
National Institutes of Health · USNational Institutes of Health Clinical Center · USFrederick National Laboratory for Cancer Research · US

Funding

CCR NIH HHS HHSN261200800001CNCI NIH HHS HHSN261200800001ENIAID NIH HHS HHSN272200700016I
6 · The paper itself

Abstract

purposeCertain viral infectious diseases cause systemic damage and the liver is an important organ affected directly by the virus and/or the hosts' response to the virus. Medical imaging indicates that the liver damage is heterogenous, and therefore, quantification of these changes requires analysis of the entire organ. Delineating the liver in preclinical imaging studies is a time-consuming and difficult task that would benefit from automated liver segmentation.

methodsA nonhuman primate atlas-based liver segmentation method was developed to support quantitative image analysis of preclinical research. A set of 82 computed tomography (CT) scans of nonhuman primates with associated manual contours delineating the liver was generated from normal and abnormal livers. The proposed technique uses rigid and deformable registrations, a majority vote algorithm, and image post-processing operations to automate the liver segmentation process. This technique was evaluated using Dice similarity, Hausdorff distance measures, and Bland-Altman plots.

resultsAutomated segmentation results compare favorably with manual contouring, achieving a median Dice score of 0.91. Limits of agreement from Bland-Altman plots indicate that liver changes of 3 Hounsfield units (CT) and 0.4 SUVmean (positron emission tomography) are detectable using our automated method of segmentation, which are substantially less than changes observed in the host response to these viral infectious diseases.

conclusionThe proposed atlas-based liver segmentation technique is generalizable to various sizes and species of nonhuman primates and facilitates preclinical infectious disease research studies. While the image analysis software used is commercially available and facilities with funding can access the software to perform similar nonhuman primate liver quantitative analyses, the approach can be implemented in open-source frameworks as there is nothing proprietary about these methods.

Indexed as

AlgorithmsAnimalsImage Processing, Computer-AssistedLiverLiver DiseasesPositron-Emission TomographyPrimatesResearchSoftwareTomography, X-Ray ComputedAtlas-based segmentationComputed tomographyLiverNonhuman primate research

Identifiers

PMID32648161
PMCPMC7502527
OpenAlexW3041336440

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.