ArticleScientific reports2023
Stereology neuron counts correlate with deep learning estimates in the human hippocampal subregions.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Chrono-combined aerobic-resistance exercises as therapeutic approach to reverse neurodegeneration in rat model: a detailed protocol.Frontiers in aging neuroscience · 2026Article
- Comparison of multiple quantitative strategies for neuropathologic image analyses.Journal of neuropathology and experimental neurology · 2025Article
- Midbrain cytotoxic T cells as a distinct neuropathological feature of progressive supranuclear palsy.Brain : a journal of neurology · 2025Article
- Alzheimer's disease and its co-pathologies: Implications for hippocampal degeneration, cognitive decline, and the role of APOE ε4.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Autism-associated CHD8 controls reactive gliosis and neuroinflammation via remodeling chromatin in astrocytes.Cell reports · 2024Article
- Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference.Scientific reports · 2024Article
- The prosubiculum in the human hippocampus: A rostrocaudal, feature-driven, and systematic approach.The Journal of comparative neurology · 2024Article
- Neuron collinearity differentiates human hippocampal subregions: a validated deep learning approach.Brain communications · 2024Article
- Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders.Frontiers in neuroscience · 2024Review
- High-throughput digital quantification of Alzheimer disease pathology and associated infrastructure in large autopsy studies.Journal of neuropathology and experimental neurology · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
Hippocampal subregions differ in specialization and vulnerability to cell death. Neuron death and hippocampal atrophy have been a marker for the progression of Alzheimer's disease. Relatively few studies have examined neuronal loss in the human brain using stereology. We characterize an automated high-throughput deep learning pipeline to segment hippocampal pyramidal neurons, generate pyramidal neuron estimates within the human hippocampal subfields, and relate our results to stereology neuron counts. Based on seven cases and 168 partitions, we vet deep learning parameters to segment hippocampal pyramidal neurons from the background using the open-source CellPose algorithm, and show the automated removal of false-positive segmentations. There was no difference in Dice scores between neurons segmented by the deep learning pipeline and manual segmentations (Independent Samples t-Test: t(28) = 0.33, p = 0.742). Deep-learning neuron estimates strongly correlate with manual stereological counts per subregion (Spearman's correlation (n = 9): r(7) = 0.97, p < 0.001), and for each partition individually (Spearman's correlation (n = 168): r(166) = 0.90, p <0 .001). The high-throughput deep-learning pipeline provides validation to existing standards. This deep learning approach may benefit future studies in tracking baseline and resilient healthy aging to the earliest disease progression.
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