Digital pathology in tau research: A comparison of QuPath and HALO

The application of digital pathology tools has expanded in recent years, but non-neoplastic human brain tissue presents unique challenges due to its complexity. This study evaluated HALO and QuPath tau quantification performance in the hippocampus and mid-frontal gyrus across various tauopathies. Percent positivity emerged as the most reliable measure, showing strong correlations with Braak stages and CERAD scores, outperforming object and optical densities. QuPath demonstrated superior correlations with Braak stages, while HALO excelled in aligning with CERAD scoring. However, HALO's optical density was less consistent. Paired t-tests revealed significant differences in object and optical densities between platforms, though percent positivity was consistent across both. QuPath's threshold-based object density showed similar agreement with manual counts compared to HALO's AI-dependent approach (all ρ > 0.70). Reanalysis of QuPath further improved its agreement with manual measurements and correlations with Braak and CERAD scores (all ρ > 0.70). HALO offers a user-friendly interface and excels in certain metrics but is hindered by frequent software malfunctions and more limited flexibility. In contrast, QuPath's customizable workflows and superior performance in Braak staging make it more suitable for advanced and larger-scale analyses. Overall, our study highlights the strengths and limitations of these platforms, helping guide their application in neuropathology.

Authors

Institutions

Publication Details

Journal
Journal of Neuropathology & Experimental Neurology
Published
2025-04-16
DOI
https://doi.org/10.1093/jnen/nlaf026
Citations
3
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
4.90

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Digital pathology in tau research: A comparison of QuPath and HALO

Margaret E. Flanagan, Sudha S. Seshadri, Yannick N. Wadop, Kevin F. Bieniek et al.
3 citations
Journal of Neuropathology & Experimental Neurology
Cell Image Analysis Techniques
4.90
article

Digital pathology in tau research: A comparison of QuPath and HALO

Margaret E. Flanagan, Sudha S. Seshadri, Yannick N. Wadop, Kevin F. Bieniek, Matthew B Dopler, Shahroo Etemad‐Moghadam, Gladys E. Maestre, Bernard Fongang, Ali Ghaseminejad‐Bandpey, Claire Corbett, Mohammad Alhneif, Mallory Keating, Angelique D. Gonzalez, Julie Parker‐Garza, S. Suresh Babu, K. Clarke, B Danner
article en
3 citations

Abstract

The application of digital pathology tools has expanded in recent years, but non-neoplastic human brain tissue presents unique challenges due to its complexity. This study evaluated HALO and QuPath tau quantification performance in the hippocampus and mid-frontal gyrus across various tauopathies. Percent positivity emerged as the most reliable measure, showing strong correlations with Braak stages and CERAD scores, outperforming object and optical densities. QuPath demonstrated superior correlations with Braak stages, while HALO excelled in aligning with CERAD scoring. However, HALO's optical density was less consistent. Paired t-tests revealed significant differences in object and optical densities between platforms, though percent positivity was consistent across both. QuPath's threshold-based object density showed similar agreement with manual counts compared to HALO's AI-dependent approach (all ρ > 0.70). Reanalysis of QuPath further improved its agreement with manual measurements and correlations with Braak and CERAD scores (all ρ > 0.70). HALO offers a user-friendly interface and excels in certain metrics but is hindered by frequent software malfunctions and more limited flexibility. In contrast, QuPath's customizable workflows and superior performance in Braak staging make it more suitable for advanced and larger-scale analyses. Overall, our study highlights the strengths and limitations of these platforms, helping guide their application in neuropathology.

Journal of Neuropathology & Experimental NeurologyVol. 84(8)
The University of Texas at San Antonio Health Science Center (US), The University of Texas Rio Grande Valley (US)
National Institutes of Health
Industry, innovation and infrastructure
Openalex Percentile: Top 6%
Cell Image Analysis Techniques
4.90
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.