DeNeRD: high-throughput detection of neurons for brain-wide analysis with deep learning
Abstract Mapping the structure of the mammalian brain at cellular resolution is a challenging task and one that requires capturing key anatomical features at the appropriate level of analysis. Although neuroscientific methods have managed to provide significant insights at the micro and macro level,...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2019
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/0c93a62486f44119a30a0c109bda08fa |
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| Sumario: | Abstract Mapping the structure of the mammalian brain at cellular resolution is a challenging task and one that requires capturing key anatomical features at the appropriate level of analysis. Although neuroscientific methods have managed to provide significant insights at the micro and macro level, in order to obtain a whole-brain analysis at a cellular resolution requires a meso-scopic approach. A number of methods can be currently used to detect and count cells, with, nevertheless, significant limitations when analyzing data of high complexity. To overcome some of these constraints, we introduce a fully automated Artificial Intelligence (AI)-based method for whole-brain image processing to Detect Neurons in different brain Regions during Development (DeNeRD). We demonstrate a high performance of our deep neural network in detecting neurons labeled with different genetic markers in a range of imaging planes and imaging modalities. |
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