ArticleIEEE access : practical innovations, open solutions2022
BrainSec: Automated Brain Tissue Segmentation Pipeline for Scalable Neuropathological Analysis.
Article in IEEE access : practical innovations, open solutions, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Data-driven thresholds for standardized classification of severe Alzheimer's disease neuropathology using digital neuropathology.Brain pathology (Zurich, Switzerland) · 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
- Bridging the Pathology Domain Gap: Efficiently Adapting CLIP for Pathology Image Analysis with Limited Labeled Data.Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2025Article
- Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases.Acta neuropathologica communications · 2024Article
- High-throughput digital quantification of Alzheimer disease pathology and associated infrastructure in large autopsy studies.Journal of neuropathology and experimental neurology · 2023Article
- Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-β analyses on digitized human brain slides.Journal of neuropathology and experimental neurology · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
As neurodegenerative disease pathological hallmarks have been reported in both grey matter (GM) and white matter (WM) with different density distributions, automating the segmentation process of GM/WM would be extremely advantageous for aiding in neuropathologic deep phenotyping. Standard segmentation methods typically involve manual annotations, where a trained researcher traces the delineation of GM/WM in ultra-high-resolution Whole Slide Images (WSIs). This method can be time-consuming and subjective, preventing a scalable analysis on pathology images. This paper proposes an automated segmentation pipeline (BrainSec) combining a Convolutional Neural Network (CNN) module for segmenting GM/WM regions and a post-processing module to remove artifacts/residues of tissues. The final output generates XML annotations that can be visualized via Aperio ImageScope. First, we investigate two baseline models for medical image segmentation: FCN, and U-Net. Then we propose a patch-based approach, BrainSec, to classify the GM/WM/background regions. We demonstrate BrainSec is robust and has reliable performance by testing it on over 180 WSIs that incorporate numerous unique cases as well as distinct neuroanatomic brain regions. We also apply gradient-weighted class activation mapping (Grad-CAM) to interpret the segmentation masks and provide relevant explanations and insights. In addition, we have integrated BrainSec with an existing Amyloid-
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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.