Deep learning assisted quantitative assessment of histopathological markers of Alzheimer's disease and cerebral amyloid angiopathy.
Deep learning assisted quantitative assessment of histopathological markers of Alzheimer's disease and cerebral amyloid angiopathy.
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深度学习辅助定量评估阿尔茨海默病和脑淀粉样血管病的组织病理学标志物。
DOI：
10.1186/s40478-021-01235-1
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发表时间：
2021-08-21
影响因子：
7.1
通讯作者：
van Veluw SJ
中科院分区：
文献类型：
--
作者：
Perosa V;Scherlek AA;Kozberg MG;Smith L;Westerling-Bui T;Auger CA;Vasylechko S;Greenberg SM;van Veluw SJ
传统上，神经退行性疾病中神经病理学标记物的分析依赖于染色切片的视觉评估。由此产生的半定量分数通常在各个评估者和研究中心之间有所不同，从而限制了统计方法。为了克服这些问题，我们开发了六种基于深度学习的模型，可以识别阿尔茨海默病 (AD) 和脑淀粉样血管病 (CAA) 的一些最具特征性的标志物。基于深度学习的模型经过训练，可以差异化检测实质 β 淀粉样蛋白 (Aβ) 斑块、血管 Aβ 沉积、铁和钙沉积、反应性星形胶质细胞、小胶质细胞以及纤维蛋白外渗。这些模型在 AD 和 CAA 患者大脑的数字化组织病理学切片上进行训练，使用的工作流程允许神经病理学专家在基于云的图形界面上训练卷积神经网络 (CNN)。所有模型的验证表明，与三位独立的专家评估员相比，模型的性能非常好到优秀。此外，Aβ 和铁模型与先前在同一数据集中获得的半定量分数一致，并允许使用更复杂的统计方法。例如，线性混合效应模型可用于确认先前描述的软脑膜 CAA 严重程度与皮质铁积累之间的关系。类似的方法使我们能够探索神经炎症与不同 Aβ 病理学之间的关联。所提出的工作流程对于具有病理学专业知识的研究人员来说很容易实施，并且可以针对其他组织病理学标记进行定制。对组织病理学切片进行深度学习辅助分析的实施可能会促进跨研究中心神经病理学标志物评估的标准化，这将使神经退行性疾病中的特定病理生理学问题能够以统一的方式和更大范围内得到解决。在线版本包含可在 10.1186/s40478-021-01235-1 获取的补充材料。
Traditionally, analysis of neuropathological markers in neurodegenerative diseases has relied on visual assessments of stained sections. Resulting semiquantitative scores often vary between individual raters and research centers, limiting statistical approaches. To overcome these issues, we have developed six deep learning-based models, that identify some of the most characteristic markers of Alzheimer’s disease (AD) and cerebral amyloid angiopathy (CAA). The deep learning-based models are trained to differentially detect parenchymal amyloid β (Aβ)-plaques, vascular Aβ-deposition, iron and calcium deposition, reactive astrocytes, microglia, as well as fibrin extravasation. The models were trained on digitized histopathological slides from brains of patients with AD and CAA, using a workflow that allows neuropathology experts to train convolutional neural networks (CNNs) on a cloud-based graphical interface. Validation of all models indicated a very good to excellent performance compared to three independent expert human raters. Furthermore, the Aβ and iron models were consistent with previously acquired semiquantitative scores in the same dataset and allowed the use of more complex statistical approaches. For example, linear mixed effects models could be used to confirm the previously described relationship between leptomeningeal CAA severity and cortical iron accumulation. A similar approach enabled us to explore the association between neuroinflammation and disparate Aβ pathologies. The presented workflow is easy for researchers with pathological expertise to implement and is customizable for additional histopathological markers. The implementation of deep learning-assisted analyses of histopathological slides is likely to promote standardization of the assessment of neuropathological markers across research centers, which will allow specific pathophysiological questions in neurodegenerative disease to be addressed in a harmonized way and on a larger scale. The online version contains supplementary material available at 10.1186/s40478-021-01235-1.
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影响因子：
168.9
作者：
Ballard, Clive;Gauthier, Serge;Jones, Emma
通讯作者：
Jones, Emma
影响因子：
16.2
作者：
Cortes-Canteli, Marta;Paul, Justin;Norris, Erin H.;Bronstein, Robert;Ahn, Hyung Jin;Zamolodchikov, Daria;Bhuvanendran, Shivaprasad;Fenz, Katherine M.;Strickland, Sidney
通讯作者：
Strickland, Sidney
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
11.1
作者：
Khosravi P;Kazemi E;Imielinski M;Elemento O;Hajirasouliha I
通讯作者：
Hajirasouliha I
影响因子：
1.9
作者：
FUKUSHIMA, K
通讯作者：
FUKUSHIMA, K