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Currently, this information has to be extracted by semimanual procedures, which severely limit the scalability of the experiments.<\/jats:p><jats:p>In this contribution, we propose a supervised machine learning approach for the detailed reconstruction of the geometry of fibres inside a peripheral nerve based on its high\u2010resolution serial section images. Learning from sparse expert annotations, the algorithm traces myelinated axons, even across the nodes of Ranvier. The latter are detected automatically.<\/jats:p><jats:p>The approach is based on classifying the myelinated membranes in a supervised fashion, closing the membrane gaps by solving an assignment problem, and classifying the closed gaps for the nodes of Ranvier detection.<\/jats:p><jats:p>The algorithm has been validated on two very different datasets: (i) rat vagus nerve subvolume, SBFSEM microscope, 200 \u00d7 200 \u00d7 200 nm resolution, (ii) rat sensory branch subvolume, confocal microscope, 384 \u00d7 384 \u00d7 800 nm resolution. For the first dataset, the algorithm correctly reconstructed 88% of the axons (241 out of 273) and achieved 92% accuracy on the task of Ranvier node detection. For the second dataset, the gap closing algorithm correctly closed 96.2% of the gaps, and 55% of axons were reconstructed correctly through the whole volume. On both datasets, training the algorithm on a small data subset and applying it to the full dataset takes a fraction of the time required by the currently used semiautomated protocols. Our software, raw data and ground truth annotations are available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/hci.iwr.uni-heidelberg.de\/Benchmarks\/\">http:\/\/hci.iwr.uni\u2010heidelberg.de\/Benchmarks\/<\/jats:ext-link>. The development version of the code can be found at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/RWalecki\/ATMA\">https:\/\/github.com\/RWalecki\/ATMA<\/jats:ext-link>.<\/jats:p>","DOI":"10.1111\/jmi.12266","type":"journal-article","created":{"date-parts":[[2015,6,12]],"date-time":"2015-06-12T16:18:19Z","timestamp":1434125899000},"page":"143-154","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Automated tracing of myelinated axons and detection of the nodes of Ranvier in serial images of peripheral nerves"],"prefix":"10.1111","volume":"259","author":[{"given":"A.","family":"KRESHUK","sequence":"first","affiliation":[{"name":"Heidelberg Collaboratory for Image Processing (HCI) Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg  Heidelberg Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R.","family":"WALECKI","sequence":"additional","affiliation":[{"name":"Heidelberg Collaboratory for Image Processing (HCI) Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg  Heidelberg Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"U.","family":"KOETHE","sequence":"additional","affiliation":[{"name":"Heidelberg Collaboratory for Image Processing (HCI) Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg  Heidelberg Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"GIERTHMUEHLEN","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery University Medical Center  Freiburg Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D.","family":"PLACHTA","sequence":"additional","affiliation":[{"name":"Department of Microsystems Engineering, University of Freiburg  Freiburg Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"GENOUD","sequence":"additional","affiliation":[{"name":"Facility for Advanced Imaging and Microscopy, Friedrich Miescher Institute for Biomedical Research (FMI)  Basel Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"HAASTERT\u2010TALINI","sequence":"additional","affiliation":[{"name":"Institute of Neuroanatomy, Hannover Medical School Hannover, Germany and Center for Systems Neurosciences (ZSN)  Hannover Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.A.","family":"HAMPRECHT","sequence":"additional","affiliation":[{"name":"Heidelberg Collaboratory for Image Processing (HCI) Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg  Heidelberg Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2015,6,12]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2011.11.004"},{"key":"e_1_2_6_3_1","first-page":"466","article-title":"Globally optimal multi\u2010target tracking on a hexagonal lattice","volume":"6311","author":"Andriyenko A.","year":"2010","journal-title":"IEEE Eur. 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