Evidence map›Paper›PMID 34496667›Full record

ArticleCartilage2021

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning.

Kevin A Thomas, Dominik Krzemiński, Łukasz Kidziński, Rohan Paul, Elka B Rubin, Eni Halilaj, Marianne S Black, Akshay Chaudhari, Garry E Gold, Scott L Delp

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

Article in Cartilage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.3field-weighted citation impact, top 43% 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

8 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Article
  3. Exercise-Induced Changes in Knee Cartilage In Vivo: Comparing MRI Sequences.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025
    Article
  4. Article
  5. Observational
  6. Review
  7. Article
  8. Latest advancements in imaging techniques in OA.Therapeutic advances in musculoskeletal disease · 2022
    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

10 authors at 3 institutions in 2 countries.

Kevin A ThomasDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.ORCID 0000-0002-3546-330X
Dominik KrzemińskiCardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, UK.
Łukasz KidzińskiDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Rohan PaulDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Elka B RubinDepartment of Radiology, Stanford University, Stanford, CA, USA.
Eni HalilajDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
Marianne S BlackDepartment of Radiology, Stanford University, Stanford, CA, USA.
Akshay ChaudhariDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Garry E GoldDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Scott L DelpDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Stanford University · USCardiff University · GBCarnegie Mellon University · US

Funding

Training CoreU54EB020405 · NIBIB · STANFORD UNIVERSITY · PI DELP, SCOTT L · 2014 to 2018
$11.6M
Rapid MRI for Evaluation of OsteoarthritisR01EB002524 · NIBIB · STANFORD UNIVERSITY · PI Akshay Chaudhari, Garry E Gold · 2003 to 2026
$10.1M
TR&D Project 3: OpenSim for PredictionP41EB027060 · NIBIB · STANFORD UNIVERSITY · PI Joy P Ku · 2020 to 2026
$9.7M
Advanced MR Imaging of Early OsteoarthritisK24AR062068 · NIAMS · STANFORD UNIVERSITY · PI GOLD, GARRY E · 2012 to 2022
$1.7M
NIAMS NIH HHS K24 AR062068NIBIB NIH HHS P41 EB027060NIBIB NIH HHS R01 EB002524NIBIB NIH HHS U54 EB020405
6 · The paper itself

Abstract

objectiveWe evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically.

designWe trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries. Subregional T2 values and four-year changes were calculated using a radiologist's segmentations (Reader 1) and the model's segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert (Reader 2) for comparison.

resultsModel segmentations agreed with Reader 1 segmentations with a Dice score of 0.85 ± 0.03. The model's estimated T2 values for individual subregions agreed with those of Reader 1 with an average Spearman correlation of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model's estimated four-year change in T2 for individual subregions agreed with Reader 1 with an average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice score (0.85 vs. 0.75) and subregional T2 values.

conclusionsAssessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.

Indexed as

Cartilage, ArticularDeep LearningHumansKneeKnee JointSoftwarecartilage segmentationdeep learningneural networkosteoarthritisT2 map MRI

Identifiers

PMID34496667
PMCPMC8808775
OpenAlexW3115628870

What OpenQuestion holds

Textmetadata
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.