Application of the U-Net Deep Learning Model for Segmenting Single-Photon Emission Computed Tomography Myocardial Perfusion Images

The findings demonstrate that the U-Net model effectively addresses some segmentation challenges in MPI, facilitating improved diagnosis and analysis of mega data.

Authors

M. Alajmi
Ali Mayya (ORCID: https://orcid.org/0000-0002-8262-0072)
Ahmad Alenezi (ORCID: https://orcid.org/0000-0002-8018-3585)
Hamad Alhamad (ORCID: https://orcid.org/0009-0003-6765-5634)
Wegdan Almutairi
Dana Alaradah

Institutions

Latakia University (SY)
Jahra Hospital (KW)
Kuwait University (KW)
Ministry of Health (KW)

Publication Details

Journal: Diagnostics
Published: 2024-12-20
DOI: https://doi.org/10.3390/diagnostics14242865
Citations: 2
Primary Topic: Advanced X-ray and CT Imaging
Type: article
Field-Weighted Citation Impact: 0.37

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article

Application of the U-Net Deep Learning Model for Segmenting Single-Photon Emission Computed Tomography Myocardial Perfusion Images

M. Alajmi, Ali Mayya, Ahmad Alenezi, Hamad Alhamad et al.

Dec 20, 2024

2 citations

Diagnostics

Advanced X-ray and CT Imaging

0.37

article

Application of the U-Net Deep Learning Model for Segmenting Single-Photon Emission Computed Tomography Myocardial Perfusion Images

M. Alajmi, Ali Mayya, Ahmad Alenezi, Hamad Alhamad, Wegdan Almutairi, Dana Alaradah

December 20, 2024article en

2 citations

Abstract

The findings demonstrate that the U-Net model effectively addresses some segmentation challenges in MPI, facilitating improved diagnosis and analysis of mega data.

DiagnosticsVol. 14(24)

Latakia University (SY), Jahra Hospital (KW), Kuwait University (KW), Ministry of Health (KW)

Good health and well-being

Openalex Percentile: Top 41%

Advanced X-ray and CT Imaging

0.37

DOI Open Access PDF OpenAlex

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Application of the U-Net Deep Learning Model for Segmenting Single-Photon Emission Computed Tomography Myocardial Perfusion Images — M. Alajmi, Ali Mayya, et al. · Diagnostics (2024) | TGRS Research Map | TGRS