{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:55:19Z","timestamp":1787021719786,"version":"build-2736575974"},"reference-count":41,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2015,11,4]],"date-time":"2015-11-04T00:00:00Z","timestamp":1446595200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Histochem Cytochem."],"published-print":{"date-parts":[[2016,1]]},"abstract":"<jats:p>Digital image analysis of histology sections provides reliable, high-throughput methods for neuropathological studies but data is scant in frontotemporal lobar degeneration (FTLD), which has an added challenge of study due to morphologically diverse pathologies. Here, we describe a novel method of semi-automated digital image analysis in FTLD subtypes including: Pick\u2019s disease (PiD, n=11) with tau-positive intracellular inclusions and neuropil threads, and TDP-43 pathology type C (FTLD-TDPC, n=10), defined by TDP-43-positive aggregates predominantly in large dystrophic neurites. To do this, we examined three FTLD-associated cortical regions: mid-frontal gyrus (MFG), superior temporal gyrus (STG) and anterior cingulate gyrus (ACG) by immunohistochemistry. We used a color deconvolution process to isolate signal from the chromogen and applied both object detection and intensity thresholding algorithms to quantify pathological burden. We found object-detection algorithms had good agreement with gold-standard manual quantification of tau- and TDP-43-positive inclusions. Our sampling method was reliable across three separate investigators and we obtained similar results in a pilot analysis using open-source software. Regional comparisons using these algorithms finds differences in regional anatomic disease burden between PiD and FTLD-TDP not detected using traditional ordinal scale data, suggesting digital image analysis is a powerful tool for clinicopathological studies in morphologically diverse FTLD syndromes.<\/jats:p>","DOI":"10.1369\/0022155415614303","type":"journal-article","created":{"date-parts":[[2015,11,4]],"date-time":"2015-11-04T23:29:47Z","timestamp":1446679787000},"page":"54-66","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":57,"title":["Semi-Automated Digital Image Analysis of Pick\u2019s Disease and TDP-43 Proteinopathy"],"prefix":"10.1369","volume":"64","author":[{"given":"David J.","family":"Irwin","sequence":"first","affiliation":[{"name":"Penn Frontotemporal Degeneration Center, Department of Neurology (DJI, MDB, CTM, FC, MG), University of Pennsylvania Perelman School of Medicine, University of 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