Evidence map›Paper›PMID 33283209›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2020

Cartilage Segmentation in High-Resolution 3D Micro-CT Images via Uncertainty-Guided Self-training with Very Sparse Annotation.

Hao Zheng, Susan M Motch Perrine, M Kathleen Pitirri, Kazuhiko Kawasaki, Chaoli Wang, Joan T Richtsmeier, Danny Z Chen

Abstract read
In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Embryonic cranial cartilage defects in the Fgfr3Anatomical record (Hoboken, N.J. : 2007) · 2025
    Article
  3. Article
  4. CONUNETR: A CONDITIONAL TRANSFORMER NETWORK FOR 3D MICRO-CT EMBRYONIC CARTILAGE SEGMENTATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

7 authors.

Hao ZhengDepartment of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
Susan M Motch PerrineDepartment of Anthropology, Pennsylvania State University, University Park, PA 16802, USA.
M Kathleen PitirriDepartment of Anthropology, Pennsylvania State University, University Park, PA 16802, USA.
Kazuhiko KawasakiDepartment of Anthropology, Pennsylvania State University, University Park, PA 16802, USA.
Chaoli WangDepartment of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
Joan T RichtsmeierDepartment of Anthropology, Pennsylvania State University, University Park, PA 16802, USA.
Danny Z ChenDepartment of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.

Funding

The chondrocranium in craniofacial development and diseaseR01DE027677 · NIDCR · PENNSYLVANIA STATE UNIVERSITY, THE · PI RICHTSMEIER, JOAN THERESE · 2018 to 2022
$2.6M
NIDCR NIH HHS R01 DE027677
6 · The paper itself

Abstract

Craniofacial syndromes often involve skeletal defects of the head. Studying the development of the

Indexed as

Cartilage segmentationSparse annotationUncertainty

Identifiers

PMID33283209
PMCPMC7711309

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.