Automated Deep Learning Artificial Intelligence Tool for Spleen Segmentation on CT: Defining Volume-Based Thresholds for Splenomegaly.

Perez, Alberto A; Noe-Kim, Victoria; Lubner, Meghan G; Somsen, David; Garrett, John W; Summers, Ronald M; Pickhardt, Perry J

Perez, Alberto A; Noe-Kim, Victoria; Lubner, Meghan G; Somsen, David; Garrett, John W; Summers, Ronald M; Pickhardt, Perry J.

Afiliación

Perez AA; Department of Radiology, The University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252.
Noe-Kim V; Mallinckrodt Institute of Radiology, St. Louis, MO.
Lubner MG; Department of Radiology, The University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252.
Somsen D; Department of Radiology, The University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252.
Garrett JW; Department of Radiology, The University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252.
Summers RM; Department of Radiology, The University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252.
Pickhardt PJ; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology, and Imaging Sciences, NIH Clinical Center, Bethesda, MD.

AJR Am J Roentgenol ; 221(5): 611-619, 2023 Nov.

Article en En | MEDLINE | ID: mdl-37377359

Palabras clave

CT; artificial intelligence; splenomegaly; volumetrics; volumetry

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies Idioma: En Revista: AJR Am J Roentgenol Año: 2023 Tipo del documento: Article

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