Inglis, AndrewCruz, LuisRoe, Dan L.Stanley, Harry EugeneRosene, Douglas L.Urbanc, Brigita2020-04-022020-04-022008-06-01A. Inglis, L. Cruz, D.L. Roe, H.E. Stanley, D.L. Rosene, B. Urbanc. 2008. "Automated identification of neurons and their locations." JOURNAL OF MICROSCOPY, Volume 230, Issue 3, pp. 339 - 352 (14). https://doi.org/10.1111/j.1365-2818.2008.01992.x0022-2720https://hdl.handle.net/2144/39938"This is the peer reviewed version of the following article: A. Inglis, L. Cruz, D.L. Roe, H.E. Stanley, D.L. Rosene, B. Urbanc. 2008. "Automated identification of neurons and their locations." JOURNAL OF MICROSCOPY, Volume 230, Issue 3, pp. 339 - 352, which has been published in final form at https://doi.org/10.1111/j.1365-2818.2008.01992.x. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions."Individual locations of many neuronal cell bodies (>10^4) are needed to enable statistically significant measurements of spatial organization within the brain such as nearest‐neighbour and microcolumnarity measurements. In this paper, we introduce an Automated Neuron Recognition Algorithm (ANRA) which obtains the (x, y) location of individual neurons within digitized images of Nissl‐stained, 30 μm thick, frozen sections of the cerebral cortex of the Rhesus monkey. Identification of neurons within such Nissl‐stained sections is inherently difficult due to the variability in neuron staining, the overlap of neurons, the presence of partial or damaged neurons at tissue surfaces, and the presence of non‐neuron objects, such as glial cells, blood vessels, and random artefacts. To overcome these challenges and identify neurons, ANRA applies a combination of image segmentation and machine learning. The steps involve active contour segmentation to find outlines of potential neuron cell bodies followed by artificial neural network training using the segmentation properties (size, optical density, gyration, etc.) to distinguish between neuron and non‐neuron segmentations. ANRA positively identifies 86 ± 5% neurons with 15 ± 8% error (mean ± SD) on a wide range of Nissl‐stained images, whereas semi‐automatic methods obtain 80 ± 7%/17 ± 12%. A further advantage of ANRA is that it affords an unlimited increase in speed from semi‐automatic methods, and is computationally efficient, with the ability to recognize ∼100 neurons per minute using a standard personal computer. ANRA is amenable to analysis of huge photo‐montages of Nissl‐stained tissue, thereby opening the door to fast, efficient and quantitative analysis of vast stores of archival material that exist in laboratories and research collections around the world.p. 339 - 352en-USScience & technologyMicroscopyActive contoursArtificial neural networkNeuron identificationNissl-stainTrainingConfocal microscope imagesSpatial-distributionTissue-sectionsAnalysis systemCell-nucleiSegmentationBrainStereologyCortexAlgorithmsAnimalsAutomationCell nucleolusCell nucleusImage processing, computer-assistedMacaca mulattaNeuronsNissl bodiesCondensed matter physicsMaterials engineeringBiochemistry and cell biologyArticle10.1111/j.1365-2818.2008.01992.x57893