Evidence map›Paper›PMID 31259202›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2019

Atlas-based algorithm for automatic anatomical measurements in the knee.

Michael Brehler, Gaurav Thawait, Jonathan Kaplan, John Ramsay, Miho J Tanaka, Shadpour Demehri, Jeffrey H Siewerdsen, Wojciech Zbijewski

Open access · bronzeAbstract read
In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 28% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. 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

8 authors at 2 institutions in 1 country.

Michael BrehlerJohns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States.ORCID https://orcid.org/0000-0002-4663-6587
Gaurav ThawaitJohns Hopkins University, Russell H. Morgan Department of Radiology, Baltimore, Maryland, United States.
Jonathan KaplanU.S. Army Natick Soldier Systems Center, Natick, Massachusetts, United States.
John RamsayU.S. Army Natick Soldier Systems Center, Natick, Massachusetts, United States.
Miho J TanakaJohns Hopkins University, Department of Orthopaedic Surgery, Baltimore, Maryland, United States.
Shadpour DemehriJohns Hopkins University, Russell H. Morgan Department of Radiology, Baltimore, Maryland, United States.
Jeffrey H SiewerdsenJohns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States.ORCID https://orcid.org/0000-0001-6644-1317
Wojciech ZbijewskiJohns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States.ORCID https://orcid.org/0000-0001-7593-5394
Johns Hopkins University · USCombat Capabilities Development Command Soldier Center · US

Funding

Quantitative high resolution cone beam CT for assessment of bone and joint healthR01EB018896 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI ZBIJEWSKI, WOJCIECH BARTOSZ · 2014 to 2017
$2.0M
NIBIB NIH HHS R01 EB018896
6 · The paper itself

Abstract

We present an algorithm for automatic anatomical measurements in tomographic datasets of the knee. The algorithm uses a set of atlases, each consisting of a knee image, surface segmentations of the bones, and locations of landmarks required by the anatomical metrics. A multistage volume-to-volume and surface-to-volume registration is performed to transfer the landmarks from the atlases to the target volume. Manual segmentation of the target volume is not required in this approach. Metrics were computed from the transferred landmarks of a best-matching atlas member (different for each bone), identified based on a mutual information criterion. Leave-one-out validation of the algorithm was performed on 24 scans of the knee obtained using extremity cone-beam computed tomography. Intraclass correlation (ICC) between the algorithm and the expert who generated atlas landmarks was above 0.95 for all metrics. This compares favorably to inter-reader ICC, which varied from 0.19 to 0.95, depending on the metric. Absolute agreement with the expert was also good, with median errors below 0.25 deg for measurements of tibial slope and static alignment, and below 0.2 mm for tibial tuberosity-trochlear groove distance and medial tibial depth. The automatic approach is anticipated to improve measurement workflow and mitigate the effects of operator experience and training on reliability of the metrics.

Indexed as

anatomical landmarksanatomical measurementsatlasautomatic measurementimage analysisimage registration

Identifiers

PMID31259202
PMCPMC6582228
OpenAlexW2951036920

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.