Union is strength: the combination of radiomics features and 3D-deep learning in a sole model increases diagnostic accuracy in demented patients: a whole brain 18FDG PET-CT analysis.
Alberto Bestetti, Barbara Zangheri, Sara Vincenzina Gabanelli, Vincenzo Parini, Carla Fornara
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引用次数: 0
Abstract
FDG PET imaging plays a crucial role in the evaluation of demented patients by assessing regional cerebral glucose metabolism. In recent years, both radiomics and deep learning techniques have emerged as powerful tools for extracting valuable information from medical images. This article aims to provide a comparative analysis of radiomics features, 3D-deep learning convolutional neural network (CNN) and the fusion of them, in the evaluation of 18F-FDG PET whole brain images in patients with dementia and normal controls.
FDG PET 成像通过评估区域脑葡萄糖代谢，在痴呆患者的评估中发挥着至关重要的作用。近年来，放射组学和深度学习技术已成为从医学图像中提取有价值信息的有力工具。本文旨在比较分析放射组学特征、三维深度学习卷积神经网络（CNN）及其融合在痴呆症患者和正常对照组 18F-FDG PET 全脑图像评估中的应用。
期刊介绍：
Nuclear Medicine Communications, the official journal of the British Nuclear Medicine Society, is a rapid communications journal covering nuclear medicine and molecular imaging with radionuclides, and the basic supporting sciences. As well as clinical research and commentary, manuscripts describing research on preclinical and basic sciences (radiochemistry, radiopharmacy, radiobiology, radiopharmacology, medical physics, computing and engineering, and technical and nursing professions involved in delivering nuclear medicine services) are welcomed, as the journal is intended to be of interest internationally to all members of the many medical and non-medical disciplines involved in nuclear medicine. In addition to papers reporting original studies, frankly written editorials and topical reviews are a regular feature of the journal.
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