Abstract
Immunohistochemistry is widely used as a gold standard to inspect tissues, characterize their structure and detect pathological alterations. As such, the joint analysis of histological images and other imaging modalities (MRI, PET) is of major interest to interpret these physical signals and establish their correspondence with the biological constitution of the tissues. However, it is challenging to provide a meaningful characterization of the signal specificity. In this paper, we propose an integrated method to quantitatively evaluate the discriminative power of imaging modalities. This method was validated using a macaque brain dataset containing: 3 immunohistochemically stained and 1 histochemically stained series, 1 photographic volume and 1 in vivo T2 weighted MRI. First, biological regions of interest (ROIs) were automatically delineated from histological sections stained for markers of interest and mapped on the target non-specific modalities through co-registration. These non-overlapping ROIs were considered ground truth for later classification. Voxels were evenly split in training and testing sets for a logistic regression model. The statistical significance of resulting accuracy scores was evaluated through null distribution simulations. Such an approach could be of major interest to assess relevant biological characteristics from various imaging modalities.
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Acknowledgements
This study was partially supported by the French National Agency for Research (ANR-2010-RFCS-003 “HD-SCT”) and by the Laboratoire d’Excellence Revive (Investissement d’Avenir; ANR-10-LABX-73). We thank Martine Guillermier, Susannah Williams, Aurore Bugi and Nicolas Souedet for their contribution to this work.
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Balbastre, Y. et al. (2016). A Quantitative Approach to Characterize MR Contrasts with Histology. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science(), vol 9556. Springer, Cham. https://doi.org/10.1007/978-3-319-30858-6_10
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DOI: https://doi.org/10.1007/978-3-319-30858-6_10
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