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Deep learning for Alzheimer's disease: Mapping large-scale histological tau protein for neuroimaging biomarker validation

التفاصيل البيبلوغرافية
العنوان: Deep learning for Alzheimer's disease: Mapping large-scale histological tau protein for neuroimaging biomarker validation
المؤلفون: Daniela Ushizima, Yuheng Chen, Maryana Alegro, Dulce Ovando, Rana Eser, WingHung Lee, Kinson Poon, Anubhav Shankar, Namrata Kantamneni, Shruti Satrawada, Edson Amaro Junior, Helmut Heinsen, Duygu Tosun, Lea T. Grinberg
المصدر: NeuroImage, Vol 248, Iss , Pp 118790- (2022)
بيانات النشر: Elsevier, 2022.
سنة النشر: 2022
المجموعة: LCC:Neurosciences. Biological psychiatry. Neuropsychiatry
مصطلحات موضوعية: Machine learning, Deep learning, Convolutional neural networks, Alzheimer's disease, Histopathology, Digital pathology, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1095-9572
68002459
Relation: http://www.sciencedirect.com/science/article/pii/S1053811921010570; https://doaj.org/toc/1095-9572
DOI: 10.1016/j.neuroimage.2021.118790
URL الوصول: https://doaj.org/article/20a536662d554a8788d68002459bc1ac
رقم الأكسشن: edsdoj.20a536662d554a8788d68002459bc1ac
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:10959572
68002459
DOI:10.1016/j.neuroimage.2021.118790