ArticleJournal of neuropathology and experimental neurology2023
Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-β analyses on digitized human brain slides.
Article in Journal of neuropathology and experimental neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations.Scientific reports · 2026Article
- Clinical and pathologic correlations of machine learning quantification of Aβ deposits across 3 brain regions of decedents with Alzheimer disease.Journal of neuropathology and experimental neurology · 2026Article
- NEUSEG: INTERPRETABLE UNSUPERVISED GM/WM SEGMENTATION IN BRAIN HISTOPATHOLOGY USING NUCLEI MORPHOMETRICS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- Digital neuropathology of neurodegenerative disorders: Foundations, research advances, and future directions.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- A machine learning approach to automate microinfarct and microhemorrhage screening in hematoxylin and eosin-stained human brain tissues.Journal of neuropathology and experimental neurology · 2025Article
- Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases.Acta neuropathologica communications · 2024Article
- Optimization of whole slide imaging scan settings for computer vision using human lung cancer tissue.PloS one · 2024Article
- Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles.Acta neuropathologica communications · 2023Article
- High-throughput digital quantification of Alzheimer disease pathology and associated infrastructure in large autopsy studies.Journal of neuropathology and experimental neurology · 2023Article
- The neuropathological landscape of Hispanic and non-Hispanic White decedents with Alzheimer disease.Acta neuropathologica communications · 2023Article
- Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels.Communications biology · 2023Article
- Survey of Neuroanatomic Sampling and Staining Procedures in Alzheimer Disease Research Center Brain Banks.Free neuropathology · 2023Article
- Review
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Computational machine learning (ML)-based frameworks could be advantageous for scalable analyses in neuropathology. A recent deep learning (DL) framework has shown promise in automating the processes of visualizing and quantifying different types of amyloid-β deposits as well as segmenting white matter (WM) from gray matter (GM) on digitized immunohistochemically stained slides. However, this framework has only been trained and evaluated on amyloid-β-stained slides with minimal changes in preanalytic variables. In this study, we evaluated select preanalytical variables including magnification, compression rate, and storage format using three digital slides scanners (Zeiss Axioscan Z1, Leica Aperio AT2, and Leica Aperio GT 450), on over 60 whole slide images, in a cohort of 14 cases having a spectrum of amyloid-β deposits. We conducted statistical comparisons of preanalytic variables with repeated measures analysis of variance evaluating the outputs of two DL frameworks for segmentation and object classification tasks. For both WM/GM segmentation and amyloid-β plaque classification tasks, there were statistical differences with respect to scanner types (p < 0.05) and magnifications (p < 0.05). Although small numbers of cases were analyzed, this pilot study highlights the significance of preanalytic variables that may alter the performance of ML algorithms.
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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.