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We use this data set to explore pre-\u03b1 (cell) islands of layer 2 in the entorhinal cortex (EC), which are early affected in Alzheimer\u2019s disease and have therefore been the focus of research for many years. They appear mostly in a round and elongated shape as shown in microscopic studies. Some studies suggested that islands may be interconnected based on analyses of their shape and size in two-dimensional (2D) space. Here, we characterized morphological features (shape, size, and distribution) of pre-\u03b1 islands in the \u2018BigBrain\u2019, based on 3D-reconstructions of gapless series of cell-body-stained sections. The EC was annotated manually, and a machine-learning tool was trained to identify and segment islands with subsequent visualization using high-performance computing (HPC). Islands were visualized as 3D surfaces and their geometry was analyzed. Their morphology was complex: they appeared to be composed of interconnected islands of different types found in 2D histological sections of EC, with various shapes in 3D. Differences in the rostral-to-caudal part of EC were identified by specific distribution and size of islands, with implications for connectivity and function of the EC. 3D compactness analysis found more round and complex islands than elongated ones. The present study represents a use case for studying large microscopic data sets. It provides reference data for studies, e.g. investigating neurodegenerative diseases, where specific alterations in layer 2 were previously reported.<\/jats:p>","DOI":"10.1007\/978-3-030-82427-3_1","type":"book-chapter","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T07:03:33Z","timestamp":1626764613000},"page":"3-21","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A High-Resolution Model of the Human Entorhinal Cortex in the \u2018BigBrain\u2019 \u2013 Use Case for Machine Learning and 3D Analyses"],"prefix":"10.1007","author":[{"given":"Sabrina","family":"Behuet","sequence":"first","affiliation":[]},{"given":"Sebastian","family":"Bludau","sequence":"additional","affiliation":[]},{"given":"Olga","family":"Kedo","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Schiffer","sequence":"additional","affiliation":[]},{"given":"Timo","family":"Dickscheid","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Brandstetter","sequence":"additional","affiliation":[]},{"given":"Philippe","family":"Massicotte","sequence":"additional","affiliation":[]},{"given":"Mona","family":"Omidyeganeh","sequence":"additional","affiliation":[]},{"given":"Alan","family":"Evans","sequence":"additional","affiliation":[]},{"given":"Katrin","family":"Amunts","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,21]]},"reference":[{"issue":"6139","key":"1_CR1","doi-asserted-by":"publisher","first-page":"1472","DOI":"10.1126\/science.1235381","volume":"340","author":"K Amunts","year":"2013","unstructured":"Amunts, K., et al.: BigBrain: an ultrahigh-resolution 3D human brain model. 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