A deep learning-based histopathology classifier for Focal Cortical Dysplasia

Vorndran J, Neuner C, Coras R, Hoffmann L, Geffers S, Honke J, Herms J, Roeber S, Hamer H, Brandner S, Hartlieb T, Pieper T, Kudernatsch M, Bien CG, Kalbhenn T, Simon M, Adle-Biassette H, Cienfuegos J, Di Giacomo R, Garbelli R, Miyata H, Mühlebner A, Raicevic S, Rauramaa T, Rogerio F, Blümcke I, Jabari S (2023)

Publication Type: Journal article

Publication year: 2023


DOI: 10.1007/s00521-023-08364-9


A light microscopy-based histopathology diagnosis of human brain specimens obtained from epilepsy surgery remains the gold standard to confirm the underlying cause of a patient’s focal epilepsy and further inform postsurgical patient management. The differential diagnosis of neocortical specimens in the realm of epilepsy surgery remains, however, challenging. Herein, we developed an open access, deep learning-based classifier to histopathologically assess whole slide microscopy images (WSI) and to automatically recognize various subtypes of Focal Cortical Dysplasia (FCD), according to the ILAE consensus classification update of 2022. We trained a convolutional neuronal network (CNN) with fully digitalized WSI of hematoxylin–eosin stainings obtained from 125 patients covering the spectrum of mild malformation of cortical development (mMCD), mMCD with oligodendroglial hyperplasia in epilepsy (MOGHE), FCD ILAE Type 1a, 2a and 2b using 414 formalin-fixed and paraffin-embedded archival tissue blocks. An additional series of 198 postmortem tissue blocks from 59 patients without neurological disorders served as control to train the CNN for homotypic frontal, temporal and occipital areas and heterotypic Brodmann areas 4 and 17, entorhinal cortex and dentate gyrus. Special stains and immunohistochemical reactions were used to comprehensively annotate the region of interest. We then programmed a novel tile extraction pipeline and graphical dashboard to visualize all areas on the WSI recognized by the CNN. Our deep learning-based classifier is able to compute 1000 × 1000 µm large tiles and recognizes 25 anatomical regions and FCD categories with an accuracy of 98.8% (F1 score = 0.82). Microscopic review of regions predicted by the network confirmed these results. This deep learning-based classifier will be made available as online web application to support the differential histopathology diagnosis in neocortical human brain specimens obtained from epilepsy surgery. It will also serve as blueprint to build a digital histopathology slide suite addressing all major brain diseases encountered in patients with surgically amenable focal epilepsy.

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Vorndran, J., Neuner, C., Coras, R., Hoffmann, L., Geffers, S., Honke, J.,... Jabari, S. (2023). A deep learning-based histopathology classifier for Focal Cortical Dysplasia. Neural Computing & Applications. https://doi.org/10.1007/s00521-023-08364-9


Vorndran, Jörg, et al. "A deep learning-based histopathology classifier for Focal Cortical Dysplasia." Neural Computing & Applications (2023).

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