Other
Automated identification of neurons and their locations
Journal of microscopy (Oxford), v 230(3), pp 339-352
11 Dec 2007
PMID: 18503659
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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-neighbor 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 micron 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 artifacts. 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[st.dev.]) 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.
Metrics
13 Record Views
20 citations in Scopus
Details
- Title
- Automated identification of neurons and their locations
- Creators
- Andrew InglisLuis CruzDan L RoeH. E StanleyDouglas L RoseneBrigita Urbanc
- Publication Details
- Journal of microscopy (Oxford), v 230(3), pp 339-352
- Publisher
- Wiley
- Grant note
- 5R01AG021133-02 / National Institutes of Health (nih_________::NIH) 5P51RR000165-35 / National Institutes of Health (nih_________::NIH)
- Resource Type
- Other
- Language
- English
- Academic Unit
- Physics
- Web of Science ID
- WOS:000256161300002
- Scopus ID
- 2-s2.0-44449152617
- Other Identifier
- 991014878101004721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Microscopy