Evidence map›Paper›PMID 34290888›Full record

ArticleApplied sciences (Basel, Switzerland)2021

Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration.

Ludovic Venet, Sarthak Pati, Michael D Feldman, MacLean P Nasrallah, Paul Yushkevich, Spyridon Bakas

Abstract read
In one paragraph

Article in Applied sciences (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Discriminating inflammation from malignancy with short-dynamic patlak parametric 18 F-FDG PET/CT.European journal of nuclear medicine and molecular imaging · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Extracting value from total-body PET/CT image data - the emerging role of artificial intelligence.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
    Review
  11. Article
  12. Article
  13. Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023
    Article
  14. Review
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

6 authors.

Ludovic VenetCenter for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Sarthak PatiCenter for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID 0000-0003-2243-8487
Michael D FeldmanDepartment of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
MacLean P NasrallahDepartment of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Paul YushkevichCenter for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Spyridon BakasCenter for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID 0000-0001-8734-6482

Funding

UPenn ADCC Biomarker CoreP30AG010124 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DETRE, JOHN A · 1991 to 2020
$35.1M
Ex Vivo Imaging of the Aging Brain to Discover Morphology/Pathology AssociationsR01AG056014 · NIA · UNIVERSITY OF PENNSYLVANIA · PI Paul A. Yushkevich · 2017 to 2026
$4.5M
Cancer imaging phenomics software suite: application to brain and breast cancerU24CA189523 · NCI · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS, KONTOS, DESPINA · 2015 to 2019
$3.2M
Adaptive Large-Scale Framework for Automatic Biomedical Image SegmentationR01EB017255 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI YUSHKEVICH, PAUL A. · 2014 to 2018
$3.0M
Biomedical Image Computing and Informatics ClusterS10OD023495 · OD · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2017
$1.9M
NCI NIH HHS U24 CA189523NIA NIH HHS P30 AG010124NIA NIH HHS R01 AG056014NIBIB NIH HHS R01 EB017255NIH HHS S10 OD023495
6 · The paper itself

Abstract

Histopathologic assessment routinely provides rich microscopic information about tissue structure and disease process. However, the sections used are very thin, and essentially capture only 2D representations of a certain tissue sample. Accurate and robust alignment of sequentially cut 2D slices should contribute to more comprehensive assessment accounting for surrounding 3D information. Towards this end, we here propose a two-step diffeomorphic registration approach that aligns differently stained histology slides to each other, starting with an initial affine step followed by estimating a deformation field. It was quantitatively evaluated on ample (

Indexed as

ANHIR challengedeformablediffeomorphicdigital pathologyhistologyhistopathologyregistration

Identifiers

PMID34290888
PMCPMC8291745

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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.