Integrating color deconvolution thresholding and weakly supervised learning for automated segmentation of neurofibrillary tangle and neuropil threads

Hyung S. Roh; David J. Irwin; Mónica Muñoz López; Maria Mercedes Iñiguez de Onzoño Martin; Ranjit Ittyerah; Sydney Lim; Madigan L. Bedard; John L. Robinson; Theresa Schuck; Emilio Artacho-Pérula; María del Mar Arroyo Jiménez; María Pilar Marcos Rabal; Francisco Javier Molina Romero; Sandra Cebada Sánchez; José Carlos Delgado González; Carlos de la Rosa-Prieto; Marta Córcoles Parada; Edward B. Lee; Daniel T. Ohm; Laura E. M. Wisse; David A. Wolk; James C. Gee; Ricardo Insausti; Paul A. Yushkevich; Min Chen

doi:10.1117/12.2651848

6 April 2023 Integrating color deconvolution thresholding and weakly supervised learning for automated segmentation of neurofibrillary tangle and neuropil threads

Hyung S. Roh, David J. Irwin, Mónica Muñoz López, Maria Mercedes Iñiguez de Onzoño Martin, Ranjit Ittyerah, Sydney Lim, Madigan L. Bedard, John L. Robinson, Theresa Schuck, Emilio Artacho-Pérula, María del Mar Arroyo Jiménez, María Pilar Marcos Rabal, Francisco Javier Molina Romero, Sandra Cebada Sánchez, José Carlos Delgado González, Carlos de la Rosa-Prieto, Marta Córcoles Parada, Edward B. Lee, Daniel T. Ohm, Laura E. M. Wisse, David A. Wolk, James C. Gee, Ricardo Insausti, Paul A. Yushkevich, Min Chen

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Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 1247103 (2023) https://doi.org/10.1117/12.2651848
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Abnormally phosphorylated tau proteins are known to be a major indicator of Alzheimer’s Disease (AD) with strong association with memory loss and cognitive decline. Automated generation of pixel-wise accurate neurofibrillary tangles (NFTs) and neuropil threads (NTs) segmentation is a challenging task, due to lack of ground truth segmentation data of these abnormal tau pathology. This problem is most prominent in the case of segmenting NTs, where the small threadlike morphology makes pixel-wise labeling a laborious task and unrealistic for large-scale studies. Lack of ground truth data poses a significant limitation for many learning-based methods to generate accurate segmentations of NFTs and NTs. This work presents an automated pipeline for pixel level segmentation of NFTs and NTs that does not rely on ground truth segmentation data. The pipeline is composed of four main steps: (1) color deconvolution is used to separate histopathology images into staining channels (DAB, Hematoxylin, and Eosin), (2) Otsu’s thresholding is used on the DAB stain channel to generate pixel level segmentation of abnormal tau proteins staining, (3) a weakly-supervised learning paradigm (WildCat), using only global descriptors of images, is used to generate density maps of potential regions of NFTs and NTs, and (4) density maps and segmentations are then integrated using connected component analysis to localize NFTs and NTs in the detected tau segmentations. Our results show high global classification accuracy for NFTs (Acc:0.96) and NTs (Acc:0.91), and statistically significant distinctions when evaluating the percent area occupied of the detected NTs relative to expert ratings of NTs severity. Qualitative assessment of the NFTs and NTs results showed accurate pixel-level segmentations of the NFTs, while modest performance for NTs.

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Hyung S. Roh, David J. Irwin, Mónica Muñoz López, Maria Mercedes Iñiguez de Onzoño Martin, Ranjit Ittyerah, Sydney Lim, Madigan L. Bedard, John L. Robinson, Theresa Schuck, Emilio Artacho-Pérula, María del Mar Arroyo Jiménez, María Pilar Marcos Rabal, Francisco Javier Molina Romero, Sandra Cebada Sánchez, José Carlos Delgado González, Carlos de la Rosa-Prieto, Marta Córcoles Parada, Edward B. Lee, Daniel T. Ohm, Laura E. M. Wisse, David A. Wolk, James C. Gee, Ricardo Insausti, Paul A. Yushkevich, and Min Chen "Integrating color deconvolution thresholding and weakly supervised learning for automated segmentation of neurofibrillary tangle and neuropil threads", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 1247103 (6 April 2023); https://doi.org/10.1117/12.2651848

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