Evidence map›Paper›PMID 34993056›Full record

ArticleQuantitative imaging in medicine and surgery2022

Validation of watershed-based segmentation of the cartilage surface from sequential CT arthrography scans.

Mary E Hall, Marianne S Black, Garry E Gold, Marc E Levenston

Open access · diamondAbstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 29% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Mary E HallDepartment of Mechanical Engineering, Stanford University, Stanford, CA, USA.
Marianne S BlackDepartment of Mechanical Engineering, Stanford University, Stanford, CA, USA.
Garry E GoldDepartment of Radiology, Stanford University, Stanford, CA, USA.
Marc E LevenstonDepartment of Mechanical Engineering, Stanford University, Stanford, CA, USA.
Stanford University · US

Funding

Advanced MR Imaging of Early OsteoarthritisK24AR062068 · NIAMS · STANFORD UNIVERSITY · PI GOLD, GARRY E · 2012 to 2022
$1.7M
NIAMS NIH HHS K24 AR062068
6 · The paper itself

Abstract

backgroundThis study investigated the utility of a 2-dimensional watershed algorithm for identifying the cartilage surface in computed tomography (CT) arthrograms of the knee up to 33 minutes after an intra-articular iohexol injection as boundary blurring increased.

methodsA 2D watershed algorithm was applied to CT arthrograms of 3 bovine stifle joints taken 3, 8, 18, and 33 minutes after iohexol injection and used to segment tibial cartilage. Thickness measurements were compared to a reference standard thickness measurement and the 3-minute time point scan.

results77.2% of cartilage thickness measurements were within 0.2 mm (1 voxel) of the thickness calculated in the reference scan at the 3-minute time point. 42% fewer voxels could be segmented from the 33-minute scan than the 3-minute scan due to diffusion of the contrast agent out of the joint space and into the cartilage, leading to blurring of the cartilage boundary. The traced watershed lines were closer to the location of the cartilage surface in areas where tissues were in direct contact with each other (cartilage-cartilage or cartilage-meniscus contact).

conclusionsThe use of watershed dam lines to guide cartilage segmentation shows promise for identifying cartilage boundaries from CT arthrograms in areas where soft tissues are in direct contact with each other.

Indexed as

arthrographyComputed tomography (CT)image processingkneesegmentation

Identifiers

PMID34993056
PMCPMC8666781
OpenAlexW3187679030

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.