Whole-brain histogram analysis and top 20% gray and white matter ratio of amyloid positron emission tomography: A comparison with the centiloid scale.
Paper
Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC
The paper is loaded when this pane is shown.
The authors' code
Python · 1 line · 0 B · Apache-2.0
__init__.py at commit 678e44d, under Apache-2.0 · at the source
Overview
- Department of Radiology and Nuclear Medicine, School of Medicine, Fukushima Medical University,1 Hikariga-Oka, Fukushima, 960-1295 Japan
- Department of Radiology, Fukushima Medical University Hospital,Fukushima, Japan
- Human Brain Research Centre, Kyoto University Graduate School of Medicine,Kyoto, Japan
- Department of Human Health Sciences, Kyoto University Graduate School of Medicine,Kyoto, Japan
- Integrative Brain Imaging Centre, National Centre of Neurology and Psychiatry,Tokyo, Japan
- Advanced Clinical Research Centre, Fukushima Medical University,Fukushima, Japan
- Department of Neurology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine,Kyoto, Japan
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine,Kyoto, Japan
- Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine,Kyoto, Japan
Abstract
Objective: To compare whole-brain histogram analysis (WBHA) of amyloid-β (Aβ) positron emission tomography (PET) using several magnetic resonance imaging (MRI)-based brain extraction (brain extraction tool [BET]) methods, to evaluate a novel quantitative analysis method, namely the Top 20% Gray and White Matter Ratio (GW-ratio), to determine appropriate thresholds, and to evaluate diagnostic performance in comparison with the Centiloid scale (CL).
Methods: We analyzed structural MRI, 18F-flutemetamol amyloid PET images, and dementia severity scores (Global Clinical Dementia Rating [G-CDR] and Mini-Mental State Examination [MMSE]) of 262 participants. For WBHA, BET was performed using structural MRI and three different BET software programs, namely High-Definition-BET (HD-BET), FMRIB Software Library (FSL), and Statistical Parametric Mapping (SPM). Skewness and mode-to-mean ratio (MMR) were measured using brain-extracted PET. The Top 20% Map was generated from SPM BET images, separated into gray and white matter voxels, and their ratio was calculated as the GW-ratio. The CL value was computed using structural MRI. Aβ-negativity or positivity was visually determined.
Results: Skewness from three BET methods was strongly negatively correlated with CL. Skewness (HD-BET) showed the strongest correlation (R = -0.9043). Moreover, the GW-ratio strongly correlated with CL (R = 0.8332), whereas MMR, particularly MMR (FSL) (R = 0.2112), showed poor correlation. All indicators significantly distinguished Aβ-negative from Aβ-positive visuals. CL had the highest area under the curve (AUC, 0.9959), followed by skewness (HD-BET) (0.9927), Top 20% GW-ratio (0.9872), and skewness (SPM) (0.9779), with no statistical difference between CL and skewness (HD-BET) (P = 0.5763). Based on mean and SD values from 118 cognitively unimpaired (G-CDR = 0, MMSE ≥ 28) and visually Aβ-negative participants, 95% cut-off limits for Aβ-negative individuals without dementia symptoms were: CL ≤ 12.9, skewness (HD-BET) ≥ 0.1769, MMR (HD-BET) ≤ 0.9372, skewness (FSL) ≥ 0.1819, MMR (FSL) ≤ 1.9132, skewness (SPM) ≥ -0.0382, MMR (SPM) ≤ 1.1274, and GW-ratio ≤ 0.1079.
Conclusion: WBHA using MRI-based BET and GW-ratio showed strong correlations with CL and demonstrated a diagnostic performance comparable to that of CL.
Supplementary Information: The online version contains supplementary material available at 10.1007/
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
Its files are read in the Code ↔ Paper reader above.
MIC-DKFZ/HD-BET
678e44d546a84de0f2a7fc245f176b82b7d912fd, 18 December 2024Availability: 1 check, the latest on 28 September 2026: the link answers
- 28 September 2026: the link answers
7 files
- HD_BET/
__init__.py , Python, 1 line - HD_BET/
checkpoint_download.py , Python, 32 lines - HD_BET/
entry_point.py , Python, 58 lines - HD_BET/
hd_bet_prediction.py , Python, 99 lines - HD_BET/
paths.py , Python, 8 lines - LICENSE, License, 201 lines
- readme.md, Text, 138 lines
Tracing map
Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.
What the map holds:
- 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
- 5 scripts, each with its path and the digest of its content;
- no match between paragraphs and code yet;
- neither the text of the paper nor the code itself.
Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.
Data
No dataset and no data link were found in the paper.
Versions
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
Version 2, 28 September 2026
- Publisher: n/a → Springer Nature
Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 5 keywords, 12 MeSH terms, 1 funder, 27 references.
Cite
This paper
Yamakuni, R., Seino, S., Yamaki, A., Shima, A., Ishikawa, H., Abe, M., Takano, H., Sawamoto, N., Sekino, H., Ishii, S., Fukushima, K., Ukon, N., Kasai, T., Narumoto, J., Hanakawa, T., Ito, H., & Parkinson’s, Alzheimer’s disease Dimensional Neuroimaging Initiative (PADNI). (2026). Whole-brain histogram analysis and top 20% gray and white matter ratio of amyloid positron emission tomography: A comparison with the centiloid scale. Annals of nuclear medicine, 40(8), 983-998. https://
BibTeX
@article{yamakuni2026who
author = {Yamakuni, Ryo and Seino, Shinya and Yamaki, Anna and Shima, Atsushi and Ishikawa, Hironobu and Abe, Mitsunari and Takano, Harumasa and Sawamoto, Nobukatsu and Sekino, Hirofumi and Ishii, Shiro and Fukushima, Kenji and Ukon, Naoyuki and Kasai, Takashi and Narumoto, Jin and Hanakawa, Takashi and Ito, Hiroshi and {Parkinson’s, Alzheimer’s disease Dimensional Neuroimaging Initiative (PADNI)}},
title = {{Whole-brain histogram analysis and top 20\% gray and white matter ratio of amyloid positron emission tomography: A comparison with the centiloid scale}},
journal = {Annals of nuclear medicine},
year = {2026},
month = may,
volume = {40},
number = {8},
pages = {983--998},
publisher = {Springer Nature},
issn = {0914-7187},
doi = {10.1007/
url = {https://
pmid = {42149370},
pmcid = {PMC13388449}
}
RIS
TY - JOUR
AU - Yamakuni, Ryo
AU - Seino, Shinya
AU - Yamaki, Anna
AU - Shima, Atsushi
AU - Ishikawa, Hironobu
AU - Abe, Mitsunari
AU - Takano, Harumasa
AU - Sawamoto, Nobukatsu
AU - Sekino, Hirofumi
AU - Ishii, Shiro
AU - Fukushima, Kenji
AU - Ukon, Naoyuki
AU - Kasai, Takashi
AU - Narumoto, Jin
AU - Hanakawa, Takashi
AU - Ito, Hiroshi
AU - Parkinson’s, Alzheimer’s disease Dimensional Neuroimaging Initiative (PADNI)
TI - Whole-brain histogram analysis and top 20% gray and white matter ratio of amyloid positron emission tomography: A comparison with the centiloid scale
T2 - Annals of nuclear medicine
J2 - Ann Nucl Med
PY - 2026
DA - 2026/
VL - 40
IS - 8
SP - 983
EP - 998
SN - 0914-7187
PB - Springer Nature
DO - 10.1007/
UR - https://
LA - en
ER -
CSL-JSON
{
"id": "10.1007/
"type": "article-journal",
"title": "Whole-brain histogram analysis and top 20% gray and white matter ratio of amyloid positron emission tomography: A comparison with the centiloid scale",
"container-title": "Annals of nuclear medicine",
"author": [
{
"family": "Yamakuni",
"given": "Ryo"
},
{
"family": "Seino",
"given": "Shinya"
},
{
"family": "Yamaki",
"given": "Anna"
},
{
"family": "Shima",
"given": "Atsushi"
},
{
"family": "Ishikawa",
"given": "Hironobu"
},
{
"family": "Abe",
"given": "Mitsunari"
},
{
"family": "Takano",
"given": "Harumasa"
},
{
"family": "Sawamoto",
"given": "Nobukatsu"
},
{
"family": "Sekino",
"given": "Hirofumi"
},
{
"family": "Ishii",
"given": "Shiro"
},
{
"family": "Fukushima",
"given": "Kenji"
},
{
"family": "Ukon",
"given": "Naoyuki"
},
{
"family": "Kasai",
"given": "Takashi"
},
{
"family": "Narumoto",
"given": "Jin"
},
{
"family": "Hanakawa",
"given": "Takashi"
},
{
"family": "Ito",
"given": "Hiroshi"
},
{
"literal": "Parkinson’s, Alzheimer’s disease Dimensional Neuroimaging Initiative (PADNI)"
}
],
"container-title-short":
"volume": "40",
"issue": "8",
"page": "983-998",
"DOI": "10.1007/
"PMID": "42149370",
"PMCID": "PMC13388449",
"ISSN": "0914-7187",
"publisher": "Springer Nature",
"URL": "https://
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
18
]
]
}
}
The tracing map gets a citation of its own once an author has validated it and it has a DOI.
Similar papers
The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.
- [1] doi:10.1002/alz.71530 [code]
- Differential associations of plasma biomarkers with Alzheimer's disease and small vessel disease: A multimodal imaging study.Journal: Alzheimer's & dementia : the journal of the Alzheimer's AssociationIn common: nnU-Net, SimpleITK, PyTorch, PET / SPECT, Alzheimer's / dementia, structural MRI / diffusion
- [2] doi:10.3389/fnins.2026.1870124 [code]
- An end-to-end pipeline for automated fetal brain segmentation and biometry from 3D SSFP MRI.Journal: Frontiers in neuroscienceIn common: nnU-Net, SimpleITK, PyTorch, structural MRI / diffusion, 1 reference
- [3] doi:10.1002/alz.71649 [code]
- Postmortem brain MRI reveals differential associations of subcortical and limbic volumes with cortical thinning and neurodegenerative pathologies.Journal: Alzheimer's & dementia : the journal of the Alzheimer's AssociationIn common: nnU-Net, SimpleITK, PyTorch, Alzheimer's / dementia, structural MRI / diffusion
- [4] doi:10.1002/hipo.70124 [code]
- Association Between Anterior Hippocampal Gyrification and Episodic Memory Performance in Neurotypical Young Adults.Journal: HippocampusIn common: nnU-Net, SimpleITK, PyTorch, structural MRI / diffusion
- [5] doi:10.1371/journal.pcbi.1014555 [code]
- Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic cardiomyopathy.Journal: PLoS computational biologyIn common: nnU-Net, SimpleITK, PyTorch, structural MRI / diffusion
- [6] doi:10.64898/2026.07.15.26357954 [code]
- Portable Ultra-Low Field MRI Deep-Learning Algorithms for White Matter Lesion Segmentation Improve Accuracy and Reflect Clinical Disability in Multiple SclerosisJournal: medRxiv (preprint)In common: nnU-Net, SimpleITK, PyTorch, structural MRI / diffusion
- [7] doi:10.1136/jnnp-2025-335884 [code]
- Diffusivity anisotropy signature of slowly expanding lesions predicts progression independent of relapse activity in multiple sclerosis.Journal: Journal of neurology, neurosurgery, and psychiatryIn common: nnU-Net, SimpleITK, PyTorch, structural MRI / diffusion
- [8] doi:10.1186/s12880-026-02335-x [code]
- Automatic lateral ventricle and choroid plexus segmentation method in infant brain MR images.Journal: BMC medical imagingIn common: nnU-Net, SimpleITK, PyTorch, structural MRI / diffusion
- [9] doi:10.3389/frai.2026.1771088 [code]
- Few-shot deployment of pretrained MRI transformers in brain imaging tasks.Journal: Frontiers in artificial intelligenceIn common: nnU-Net, SimpleITK, PyTorch, structural MRI / diffusion
- [10] doi:10.21037/qims-2026-0792 [code]
- An nnU-Net-based framework with adaptive feature representation for 3D brain tumor segmentation.Journal: Quantitative imaging in medicine and surgeryIn common: nnU-Net, SimpleITK, PyTorch, structural MRI / diffusion
Contribute
The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.
Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.
Claim this paper
Correct its record
Say what each link of this record is, remove the ones that are not the paper's, add the ones that are missing. The correction becomes a new version of the record, in its Versions section.
Validate its tracing map
You validate the map as this page shows it: 1 repository of the authors' code, each at its verified commit and with its license, 5 scripts, and 0 matches between paragraphs and code (see the Code and Map sections). It then receives a DOI on Zenodo, with you (your ORCID iD) and OSCR as its creators; the code itself is not deposited.
The map's fingerprint: sha256:5d2ab5bd781b4851…
Add the badge to its README
The badge links the code to this page. Copy one of these into the README of the paper's code: only you decide where it goes, and nothing is changed for you.
Markdown
[, paste the snippet at the top, then “Commit changes…” and, to review it first, “Create a new branch and start a pull request”. You open the pull request; OSCR asks for no permission.
Request its removal
To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).
Discussion, reproductions, activity
Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.
Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.
Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.