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AI and non-AI methods for cardiac fat segmentation reviewed

By PulseAugur Editorial · [1 sources] ·

A literature review published on arXiv details advancements in automated segmentation methods for cardiac adipose tissue using Computed Tomography (CT). The review covers both AI and non-AI approaches for segmenting Epicardial Adipose Tissue (EAT) and Pericardial Adipose Tissue (PAT), which are linked to cardiovascular diseases. While automated methods show potential for clinical use by achieving human-comparable annotation quality, challenges remain, including the need for larger public datasets and optimized thresholds for contrast-enhanced CT. AI

IMPACT Automated segmentation methods show promise for clinical applications in cardiovascular disease risk assessment.

RANK_REASON The item is a literature review published on arXiv, focusing on research methods. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI and non-AI methods for cardiac fat segmentation reviewed

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The item is a literature review published on arXiv, focusing on research methods. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Andreas W. Aspe, Jonas Jalili Pedersen, Andreas Ohrt Johansen, Klaus Fuglsang Kofoed, Kristine Aavild S{\o}rensen, Rasmus Reinhold Paulsen, Josefine Vilsb{\o}ll Sundgaard ·

    Automated Cardiac Adipose Tissue Segmentation in Computed Tomography: A Literature Review

    arXiv:2607.16992v1 Announce Type: cross Abstract: This review provides an overview of recent advancements in automated segmentation methods on Computed Tomography (CT) for two types of cardiac fat: Epicardial adipose Tissue (EAT) and Pericardial Adipose Tissue (PAT). These fat de…

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