ArticleEJNMMI research2021
Convolutional neural network-based automatic heart segmentation and quantitation in
Article in EJNMMI research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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.
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Who cites it
5 citing papers in PubMed.
- Diagnostic Efficacy ofDiagnostics (Basel, Switzerland) · 2024Article
- Absolute quantitation of sympathetic nerve activity using [EJNMMI reports · 2024Article
- Are nuclear medicine images quantified in 2D and 3D equally functional?Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Three-Dimensional Heart Segmentation and Absolute Quantitation of CardiacAnnals of nuclear cardiology · 2023Article
- Methods of calculatingAnnals of nuclear medicine · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSince three-dimensional segmentation of cardiac region in
methodsWe assessed 48 patients (aged 68.4 ± 11.7 years) with heart and neurological diseases, including chronic heart failure, dementia with Lewy bodies, and Parkinson's disease. All patients were assessed by early and late
resultsThe CNN segmented the cardiac region in patients with normal and reduced uptake. The CNN-based SPECT heart counts significantly correlated with conventional planar heart counts with and without background correction and a planar heart-to-mediastinum ratio (R
conclusionsThe CNN could create segmentation from SPECT images, and average heart counts and WR were reliably calculated three-dimensionally, which might be a novel approach to quantifying SPECT images of innervation.
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