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Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

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Tang, Ziqi, Chuang, Kangway V., DeCarli, Charles, Jin, Lee-Way, Beckett, Laurel, Keiser, Michael J., Dugger, Brittany N.·2019·Nature Communications
DOI 10.1038/s41467-019-10212-1

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Abstract

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies—amyloid plaques and cerebral amyloid angiopathy—in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist’s ability suggests a route to neuropathologic deep phenotyping.

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  1. Dickson, D. W. The pathogenesis of senile plaques. J. Neuropathol. Exp. Neurol. 56, 321–339 (1997). · DOI · Paper page
  2. Fillenbaum, G. G. et al. Consortium to establish a registry for Alzheimer’s Disease (CERAD): the first twenty years. Alzheimers Dement. 4, 96–109 (2008). · DOI · Paper page
  3. Tiraboschi, P., Hansen, L. A., Thal, L. J. & Corey-Bloom, J. The importance of neuritic plaques and tangles to the development and evolution of AD. Neurology 62, 1984–1989 (2004). · DOI · Paper page
  4. Greenberg, S. M. & Vonsattel, J. P. G. Diagnosis of cerebral amyloid angiopathy—sensitivity and specificity of cortical biopsy. Stroke 28, 1418–1422 (1997). · DOI · Paper page
  5. Shinkai, Y. et al. Amyloid beta-protein deposition in the leptomeninges and cerebral cortex. Ann. Neurol. 42, 899–908 (1997). · DOI
  6. Dickson, T. C. & Vickers, J. C. The morphological phenotype of beta-amyloid plaques and associated neuritic changes in Alzheimer’s disease. Neuroscience 105, 99–107 (2001). · DOI · Paper page
  7. Dugger, B. N. et al. Rapid eye movement sleep behavior disorder and subtypes in autopsy-confirmed dementia with Lewy bodies. Mov. Disord. 27, 72–78 (2012). · DOI
  8. Lewis, J. & Dickson, D. W. Propagation of tau pathology: hypotheses, discoveries, and yet unresolved questions from experimental and human brain studies. Acta Neuropathol. 131, 27–48 (2016). · DOI · Paper page
  9. Neltner, J. H. et al. Digital pathology and image analysis for robust high-throughput quantitative assessment of Alzheimer disease neuropathologic changes. J. Neuropathol. Exp. Neurol. 71, 1075–1085 (2012). · DOI
  10. Taylor, C. R. & Levenson, R. M. Quantification of immunohistochemistry–issues concerning methods, utility and semiquantitative assessment II. Histopathology 49, 411–424 (2006). · DOI · Paper page
  11. Howard, V. & Reed, M. Unbiased Stereology: Three-dimensional Measurement in Microscopy. (Garland Science, New York, NY, USA, 2004). · DOI · Paper page
  12. Perl, D. P. et al. Practical approaches to stereology in the setting of aging- and disease-related brain banks. J. Chem. Neuroanat. 20, 7–19 (2000). · DOI
  13. Shen, D., Wu, G. & Suk, H.-I. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221–248 (2017). · DOI · Paper page
  14. Lu, D. et al. Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images. Sci. Rep. 8, 5697 (2018). · DOI · Paper page
  15. Ahmed, M. R. et al. Neuroimaging and machine learning for dementia diagnosis: recent advancements and future. Prospects IEEE Rev. Biomed. Eng. 12, 19–33 (2018). · DOI · Paper page
  16. Kautzky, A. et al. Prediction of autopsy verified neuropathological change of Alzheimer’s disease using machine learning and MRI. Front. Aging Neurosci. 10, 406 (2018). · DOI
  17. Beebe-Wang, N., Celik, S. & Lee, S.-I. MD-AD: Multi-task deep learning for Alzheimer’s disease neuropathology. Preprint at https://www.biorxiv.org/content/10.1101/331942v1 (2018). · DOI
  18. Ciregan, D., Meier, U. & Schmidhuber, J. Multi-column deep neural networks for image classification. In 2012 IEEE Conference on Computer Vision and Pattern Recognition 3642–3649 (2012). · DOI · Paper page
  19. Simard, P. Y., Steinkraus, D. & Platt, J. C. Best practices for convolutional neural networks applied to visual document analysis. In Proc. Seventh International Conference on Document Analysis and Recognition, 2003. 958–963 (IEEE, Edinburgh, UK, 2003).
  20. He, H. & Garcia, E. A. Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 9, 1263–1284 (2008). · DOI · Paper page
  21. Wang, T., Wu, D. J., Coates, A. & Ng, A. Y. End-to-end text recognition with convolutional neural networks. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) 3304–3308 (2012).
  22. Vinters, H. V. Cerebral amyloid angiopathy: a critical review. Stroke 18, 311–324 (1987). · DOI · Paper page
  23. Iwamoto, N., Nishiyama, E., Ohwada, J. & Arai, H. Distribution of amyloid deposits in the cerebral white matter of the Alzheimer’s disease brain: relationship to blood vessels. Acta Neuropathol. 93, 334–340 (1997). · DOI
  24. Cruz-Roa, A. et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. Sci. Rep. 7, 46450 (2017). · DOI · Paper page
  25. Zeiler, M. D. & Fergus, R. Visualizing and understanding convolutional networks. In Computer Vision–ECCV 2014, Vol. 8689 (eds Fleet, D., Pajdla, T., Schiele, B. & Tuytelaars, T.) 818–833 (Springer International Publishing, 2014). · DOI · Paper page
  26. Ciompi, F. et al. The importance of stain normalization in colorectal tissue classification with convolutional networks. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) 160–163 (IEEE, Melbourne, VIC, Australia, 2017). · DOI · Paper page
  27. Marée, R. et al. Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics 32, 1395–1401 (2016). · DOI · Paper page
  28. Nalisnik, M. et al. Interactive phenotyping of large-scale histology imaging data with HistomicsML. Sci. Rep. 7, 14588 (2017). · DOI
  29. Deterding, S., Sicart, M., Nacke, L., O’Hara, K. & Dixon, D. Gamification. Using game-design elements in non-gaming contexts. In CHI ’11 Extended Abstracts on Human Factors in Computing Systems 2425–2428 (2011). · DOI · Paper page
  30. Hamari, J. & Eranti, V. Framework for designing and evaluating game achievements. Digra Conference (2011).
  31. Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at https://arxiv.org/abs/1409.1556 (2014). · arXiv
  32. Cullen, K. M., Kócsi, Z. & Stone, J. Microvascular pathology in the aging human brain: evidence that senile plaques are sites of microhaemorrhages. Neurobiol. Aging 27, 1786–1796 (2006). · DOI · Paper page
  33. Majocha, R. E., Benes, F. M., Reifel, J. L., Rodenrys, A. M. & Marotta, C. A. Laminar-specific distribution and infrastructural detail of amyloid in the Alzheimer disease cortex visualized by computer-enhanced imaging of epitopes recognized by monoclonal antibodies. Proc. Natl Acad. Sci. USA 85, 6182–6186 (1988). · DOI
  34. Chuang, K. V. & Keiser, M. J. Adversarial controls for scientific machine learning. ACS Chem. Biol. 13, 2819–2821 (2018). · DOI
  35. Dugger, B. N. et al. Presence of striatal amyloid plaques in parkinson’s disease dementia predicts concomitant alzheimer’s disease: usefulness for amyloid imaging. J. Park. Dis. 2, 57–65 (2012).
  36. Vinters, H. V. et al. Review: vascular dementia: clinicopathologic and genetic considerations. Neuropathol. Appl. Neurobiol. 44, 247–266 (2018). · DOI
  37. Sharma, G. & Carter, A. Artificial intelligence and the pathologist: future frenemies? Arch. Pathol. Lab. Med. 141, 622–623 (2017). · DOI
  38. Consensus recommendations for the postmortem diagnosis of Alzheimer’s disease. Neurobiol. Aging 18, S1–S2 (1997). · DOI · Paper page
  39. Hinton, L. et al. Recruitment of a community-based cohort for research on diversity and risk of dementia. Alzheimer Dis. Assoc. Disord. 24, 234–241 (2010). · DOI
  40. Reinhard, E., Ashikhmin, N., Gooch, B. & Shirley, P. Color transfer between images. IEEE Comput. Graph. Appl. 21, 34–41 (2001). · DOI · Paper page
  41. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014). · arXiv

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Metadata

Authors
Tang, Z., Chuang, K. V., DeCarli, C., Jin, L.-W., Beckett, L., Keiser, M. J., & Dugger, B. N.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-05-15
Volume
10
Issue
1
Pages
2173-2173
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-019-10212-1
OpenAlex
W2898477488
Cited by
229
References
27
FWCI
12.20
Type
journal article
Language
en
Open access
gold
Licence
Funders
  • China Scholarship Council (N/A)
  • U.S. Department of Health & Human Services | National Institutes of Health (P30 AG010129)
  • Paul G. Allen Family Foundation - Distinguished Investigator Award

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Tang, Z., Chuang, K. V., DeCarli, C., Jin, L.-W., Beckett, L., Keiser, M. J., & Dugger, B. N. (2019). Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline. Nature Communications. https://doi.org/10.1038/s41467-019-10212-1

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