Myocardial Segmentation of Tagged Magnetic Resonance Images with Transfer Learning Using Generative Cine-To-Tagged Dataset Transformation.

Arnaud P Dhaene, Michael Loecher, Alexander J Wilson, Daniel B Ennis

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

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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

1 citing paper in PubMed.

AI in MRI: Computational Frameworks for a Faster, Optimized, and Automated Imaging Workflow.Bioengineering (Basel, Switzerland) · 2023
Article

4 · The record

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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.

Arnaud P DhaeneDepartment of Radiology, Stanford University, Stanford, CA 94305, USA.

Michael LoecherDepartment of Radiology, Stanford University, Stanford, CA 94305, USA.

Alexander J WilsonDepartment of Radiology, Stanford University, Stanford, CA 94305, USA.

Daniel B EnnisDepartment of Radiology, Stanford University, Stanford, CA 94305, USA.

Funding

Using Atrial Mechanics To Identify Fibrosis In Patients with Atrial FibrillationR01HL152256 · NHLBI · STANFORD UNIVERSITY · PI ENNIS, DANIEL B · 2020 to 2023

$2.8M

A New Framework for Understanding the Mechanisms of Diastolic DysfunctionR01HL131823 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ENNIS, DANIEL B · 2017 to 2020

$2.3M

National Heart Lung and Blood Institute HL131823NHLBI NIH HHS R01 HL131823NHLBI NIH HHS R01 HL152256

6 · The paper itself

Abstract

The use of deep learning (DL) segmentation in cardiac MRI has the potential to streamline the radiology workflow, particularly for the measurement of myocardial strain. Recent efforts in DL motion tracking models have drastically reduced the time needed to measure the heart's displacement field and the subsequent myocardial strain estimation. However, the selection of initial myocardial reference points is not automated and still requires manual input from domain experts. Segmentation of the myocardium is a key step for initializing reference points. While high-performing myocardial segmentation models exist for

Indexed as

cardiac MRIdataset augmentationdeep learningdomain transformationMRI taggingmyocardial segmentation

Identifiers

PMID36829660

DOI10.3390/bioengineering10020166

PMCPMC9952238

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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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.