深度学习卷积神经网络（DLCNN）：释放淋巴瘤 18F-FDG PET/CT 的潜能。

IF 2 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

American journal of nuclear medicine and molecular imaging Pub Date : 2021-08-15 eCollection Date: 2021-01-01

Ke Li, Ran Zhang, Weibo Cai

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引用次数: 0

摘要

本视角简要回顾了 18F-FDG PET/CT 在淋巴瘤临床治疗中的应用，以及在这些应用中对病灶分割的需求。它讨论了现有分割技术的局限性，以及使用深度学习卷积神经网络（DLCNN）完成淋巴瘤自动分割和特征描述的巨大潜力。最后，作者分享了对需要解决的技术挑战的看法，以充分释放 DLCNN 和 18F-FDG PET/CT 在淋巴瘤诊断、预后和治疗中的潜力。

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本刊更多论文

Deep learning convolutional neural network (DLCNN): unleashing the potential of ¹⁸F-FDG PET/CT in lymphoma.

This perspective briefly reviewed the applications of ¹⁸F-FDG PET/CT in the clinical management of lymphoma and the need for lesion segmentation in those applications. It discussed the limitations of existing segmentation technologies and the great potential of using deep learning convolutional neural network (DLCNN) to accomplish automatic lymphoma segmentation and characterizations. Finally, the authors shared perspectives on the technical challenges that need to be addressed to fully unleash the potential of DLCNN and ¹⁸F-FDG PET/CT in the diagnosis, prognosis, and treatment of lymphoma.

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来源期刊

American journal of nuclear medicine and molecular imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

自引率

4.00%

发文量

期刊介绍： The scope of AJNMMI encompasses all areas of molecular imaging, including but not limited to: positron emission tomography (PET), single-photon emission computed tomography (SPECT), molecular magnetic resonance imaging, magnetic resonance spectroscopy, optical bioluminescence, optical fluorescence, targeted ultrasound, photoacoustic imaging, etc. AJNMMI welcomes original and review articles on both clinical investigation and preclinical research. Occasionally, special topic issues, short communications, editorials, and invited perspectives will also be published. Manuscripts, including figures and tables, must be original and not under consideration by another journal.