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A vendor-agnostic, PACS integrated, and DICOM-compatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow.

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第一作者： Lei,Zhang

第一单位： School of Medicine, University of Maryland, Baltimore, MD, United States.

作者： Lei,Zhang ^[1] ; Wayne,LaBelle ^[1] ; Mathias,Unberath ^[2] ; Haomin,Chen ^[2] ; Jiazhen,Hu ^[2] ; Guang,Li ^[1] ; David,Dreizin ^[1]

作者单位： School of Medicine, University of Maryland, Baltimore, MD, United States. ^[1] Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States. ^[2]

关键词 DICOM OHIF PACS artificial intelligence computed tomography deep learning nnU-net quantitative visualization

DOI 10.3389/fmed.2023.1241570

PMID 37954555

发布时间 2024-10-12

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Frontiers in medicine

2023年10卷

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Frontiers in medicine

2023年10卷

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A vendor-agnostic, PACS integrated, and DICOM-compatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow.

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A vendor-agnostic, PACS integrated, and DICOM-compatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow.

Frontiers in medicine

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Frontiers in medicine

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