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Deep learning for Alzheimer's disease: Mapping large-scale histological tau protein for neuroimaging biomarker validation.
Ushizima, Daniela; Chen, Yuheng; Alegro, Maryana; Ovando, Dulce; Eser, Rana; Lee, WingHung; Poon, Kinson; Shankar, Anubhav; Kantamneni, Namrata; Satrawada, Shruti; Junior, Edson Amaro; Heinsen, Helmut; Tosun, Duygu; Grinberg, Lea T.
Afiliación
  • Ushizima D; Bakar Institute for Computational Health Sciences, University of California San Francisco, CA, USA; Berkeley Institute for Data Science, University of California Berkeley, CA, USA; Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Chen Y; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Alegro M; Bakar Institute for Computational Health Sciences, University of California San Francisco, CA, USA; Berkeley Institute for Data Science, University of California Berkeley, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Ovando D; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Eser R; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Lee W; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Poon K; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Shankar A; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Kantamneni N; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Satrawada S; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Junior EA; University of Sao Paulo Medical School, Sao Paulo, Brazil.
  • Heinsen H; University of Sao Paulo Medical School, Sao Paulo, Brazil; Julius-Maximilians University Würzburg, Würzburg, Germany.
  • Tosun D; Department of Radiology, University of California San Francisco, San Francisco, CA, USA; Veterans Affairs San Francisco, CA, USA.
  • Grinberg LT; Bakar Institute for Computational Health Sciences, University of California San Francisco, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA; University of Sao Paulo Medical School, Sao Paulo, Brazil; Department of Pathology, University of California Sa
Neuroimage ; 248: 118790, 2022 03.
Article en En | MEDLINE | ID: mdl-34933123
Abnormal tau inclusions are hallmarks of Alzheimer's disease and predictors of clinical decline. Several tau PET tracers are available for neurodegenerative disease research, opening avenues for molecular diagnosis in vivo. However, few have been approved for clinical use. Understanding the neurobiological basis of PET signal validation remains problematic because it requires a large-scale, voxel-to-voxel correlation between PET and (immuno) histological signals. Large dimensionality of whole human brains, tissue deformation impacting co-registration, and computing requirements to process terabytes of information preclude proper validation. We developed a computational pipeline to identify and segment particles of interest in billion-pixel digital pathology images to generate quantitative, 3D density maps. The proposed convolutional neural network for immunohistochemistry samples, IHCNet, is at the pipeline's core. We have successfully processed and immunostained over 500 slides from two whole human brains with three phospho-tau antibodies (AT100, AT8, and MC1), spanning several terabytes of images. Our artificial neural network estimated tau inclusion from brain images, which performs with ROC AUC of 0.87, 0.85, and 0.91 for AT100, AT8, and MC1, respectively. Introspection studies further assessed the ability of our trained model to learn tau-related features. We present an end-to-end pipeline to create terabytes-large 3D tau inclusion density maps co-registered to MRI as a means to facilitate validation of PET tracers.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Proteínas tau / Enfermedad de Alzheimer / Neuroimagen / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Límite: Aged / Aged80 / Female / Humans / Male Idioma: En Revista: Neuroimage Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Proteínas tau / Enfermedad de Alzheimer / Neuroimagen / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Límite: Aged / Aged80 / Female / Humans / Male Idioma: En Revista: Neuroimage Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos