ArticleScientific reports2019
DeNeRD: high-throughput detection of neurons for brain-wide analysis with deep learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Article
- A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy.Nature methods · 2025Article
- Software and pipelines for registration and analyses of rodent brain image data in reference atlas space.Frontiers in neuroinformatics · 2025Article
- DeepSlice: rapid fully automatic registration of mouse brain imaging to a volumetric atlas.Nature communications · 2023Article
- Localization and Registration of 2D Histological Mouse Brain Images in 3D Atlas Space.Neuroinformatics · 2023Article
- Stereology neuron counts correlate with deep learning estimates in the human hippocampal subregions.Scientific reports · 2023Article
- Mesoscale microscopy and image analysis tools for understanding the brain.Progress in biophysics and molecular biology · 2022Review
- An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset.Scientific data · 2021Article
- A deep learning algorithm for 3D cell detection in whole mouse brain image datasets.PLoS computational biology · 2021Article
- Automatic deep learning-driven label-free image-guided patch clamp system.Nature communications · 2021Article
- Developmental divergence of sensory stimulus representation in cortical interneurons.Nature communications · 2020Article
- Steps towards standardized quantification of adult neurogenesis.Nature communications · 2020Review
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Authors and funding
3 authors.
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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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.