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Abstract #2433

Automatic extraction of reproducible semi-quantitative histological metrics for MRI-histology correlations

Daniel ZL Kor1, Saad Jbabdi1, Jeroen Mollink1, Istvan N Huszar1, Menuka Pallebage- Gamarallage2, Adele Smart2, Connor Scott2, Olaf Ansorge2, Amy FD Howard1, and Karla L Miller1
1Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom, 2Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

Immunohistochemistry (IHC) images are often used as a microscopic validation tool for MRI. Acquisition of MRI and IHC in the same ex-vivo tissue sample can enable direct correlation between MRI measures and purported sources of image contrast derived from IHC, ideally at the voxel level. However, most IHC analyses still involve manual intervention (e.g. setting of thresholds). Here, we describe an end-to-end pipeline for automatically extracting stained area fraction maps to quantify the IHC stain for a given microstructural feature. The pipeline has improved reproducibility and robustness to histology artefacts, compared to manual MRI-histology analyses that suffer from inter-operator bias.

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