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REVIEW 3 major objections 5 minor 61 references

Ultra-high resolution multimodal MRI densely labelled holistic structural brain atlas

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read holiAtlas is a 0.125 mm3 multimodal brain atlas that fuses seven segmentation protocols into 350 substructure labels covering the whole intracranial cavity.

desk verdict A real, publicly released multiscale atlas built by careful integration of seven segmentation protocols — useful as a reference, but its 'ultra-high resolution' claim is resampling and its 350 labels have no quantitative accuracy validation. read the letter →

arxiv 2501.16879 v3 pith:ESFPPQKP submitted 2025-01-28 eess.IV cs.CV

José V. Manjón , Sergio Morell-Ortega , Marina Ruiz-Perez , Boris Mansencal , Edern Le Bot , Marien Gadea , Enrique Lanuza , Gwenaelle Catheline
show 11 more authors
Thomas Tourdias Vincent Planche Rémi Giraud Denis Rivière Jean-François Mangin Nicole Labra-Avila Roberto Vivo-Hernando Gregorio Rubio Fernando Aparici Maria de la Iglesia-Vaya Pierrick Coupé
This is my paper · ORCID
classification eess.IVcs.CV
keywords brainatlasMRImultimodalimagingultra-highresolutionsegmentationmultiscalelabellingthalamicnucleihippocampalsubfields
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces holiAtlas, a reference atlas of the healthy human brain built from MRI scans of 75 young adults. The atlas combines T1-weighted, T2-weighted, and white-matter-nulled contrasts at a nominal 0.125 mm3 resolution, and assigns every voxel in the intracranial cavity a label. At its finest scale it carries 350 distinct labels, produced by merging seven existing automatic segmentation protocols into one consistent hierarchy and then correcting the result with semi-automatic and manual quality control. The authors argue that this dense, multiscale, multimodal reference will make small structures such as thalamic nuclei, hippocampal subfields, and cerebellar lobules measurable in ways that 1 mm3 atlases cannot support, which may help detect neurological disease earlier.

What carries the argument

The load-bearing object is the holiBrain protocol, a hierarchical label system that integrates seven independent delineation protocols into one consistent dense labelling. The integration proceeds by overlaying each new protocol onto the vol2Brain whole-brain segmentation and resolving mismatches through a Bayesian MAP relabelling step that combines smoothed spatial probability maps with intensity likelihoods. The atlas templates themselves are produced by symmetric group-wise normalization with FireANTS, a GPU-accelerated diffeomorphic registration method, iterated ten times; the final images are the median of Laplacian-sharpened volumes and the final labels come from majority voting across the 75 registered label maps.

What would settle it

Expert neuroanatomists manually label the substructures of the holiBrain protocol in, say, 20 subjects not used to build the atlas, and the labels are propagated from the atlas to those subjects; if the overlap between the atlas-propagated labels and the manual labels is low for small structures such as thalamic nuclei or hippocampal subfields, the claim that the 350-label atlas is a valid reference would be undermined.

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Extended reading notes

Core claim

The central claim is that a holistic, densely labelled atlas of the human brain can be constructed at 0.125 mm3 resolution by fusing complementary parcellation protocols into a single consistent label system. The authors demonstrate the construction by nonlinearly registering and averaging images from 75 healthy subjects, applying seven delineation protocols, and reconciling their overlapping or conflicting labels with a spatial-intensity diffusion process that relabels voxels according to Gaussian spatial priors and intensity likelihoods. The result is a hierarchical atlas with 350 substructure labels, grouped into 54 structures, 9 tissue classes, and 1 whole-intracranial-cavity label, together with average T1w, T2w, and synthetic WMn templates. The paper's claim is that this atlas is anatomically accurate enough to serve as a reference for ultra-high-resolution segmentation and substructure-level volumetric analysis.

Load-bearing premise

The atlas is only as accurate as the fusion of seven automatic segmentation tools, whose outputs were corrected by visual inspection and manual editing rather than validated quantitatively.

Editorial extensions

If this is right

  • If the atlas is accurate, structural analyses can move from organ-level or structure-level volumes to substructure-level volumes, for example measuring atrophy of specific hippocampal subfields or thalamic nuclei rather than whole structures.
  • The multiscale hierarchy means a single atlas can support analyses at the organ, tissue, structure, and substructure levels without relabelling or coordinate changes.
  • Because the atlas is multimodal, segmentation methods trained on it can use T1w, T2w, and WMn contrasts simultaneously, potentially improving performance where only one contrast is available.
  • The public release of templates and label definitions gives other groups a common coordinate system for comparing results at 0.125 mm3 resolution.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the synthetic WMn contrast is a valid proxy for real white-matter-nulled acquisitions, the WMn template and the thalamic labels derived from it may transfer to other datasets; this transferability is testable by comparing against real WMn scans from a separate cohort, which the paper does not do.
  • The atlas's accuracy is only checked visually and by the fusion process itself; a quantitative evaluation against manual expert segmentations or histology-derived references would settle how much of the 350-label detail is real anatomy rather than algorithm agreement.
  • The 75-subject atlas spans only ages 22 to 35, so applying it to pediatric or elderly brains will encounter registration and label bias; building age-specific versions from the same pipeline is a natural next step the paper leaves implicit.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces holiAtlas, a multimodal (T1w, T2w, WMn) structural brain MRI atlas built from 75 healthy HCP subjects. The authors resample the original 0.7 mm HCP images to a 0.5 mm isotropic grid (0.125 mm^3 voxels), synthesize WMn images from T1w/T2w using a network trained on a private dataset, register all subjects to a group-wise template with FireANTS, and fuse outputs of seven automatic segmentation tools (vol2Brain, hypothalamus_seg, BrainVISA, FreeSurfer, pBrain, HIPS, CERES) with semi-automatic correction and manual QC. The final protocol has 350 substructure labels, with coarser groupings into 54 structures, 9 tissues, and 1 organ. The atlas and labels are publicly released. The central claims are that the atlas is ultra-high resolution, multimodal, and densely labelled, and that it can serve as a reference for brain segmentation and disease studies.

Significance. If the construction is sound, holiAtlas is a potentially useful community resource: it combines multiple modalities, offers hierarchical label organization, and is publicly available. The authors also make the construction scripts available via FireANTS. However, the significance is tempered by two load-bearing weaknesses: the 'ultra-high resolution' claim is based on interpolation of native 0.7 mm data rather than higher-resolution acquisition, and the WMn modality is synthetic, with no quantitative validation of its fidelity for the subsequent segmentation tasks. The atlas artifact itself is a contribution, but the paper's framing overstates its resolution and the anatomical validity of the WMn-derived labels is not established.

major comments (3)
  1. [Abstract; §2.1; §2.2; §4] The '0.125 mm3 resolution' claim is misleading. The HCP T1w and T2w images are acquired at 0.7 mm isotropic voxels (§2.1), and §2.2 states they were affine-registered to MNI152 space at 0.5 mm voxels. This is interpolation/upsampling, not a gain in true spatial resolution. The effective resolution of the T1w/T2w templates remains acquisition-limited by the original 0.7 mm sampling (and by the 1 mm working resolution of the synthesis network used for WMn). The title and abstract's 'ultra-high resolution' should be rephrased to 'resampled to 0.125 mm^3 voxels' or '0.5 mm grid' whenever the source data are not natively acquired at that resolution.
  2. [§2.2; §2.4; §4] The WMn images used to construct the atlas are synthetic, generated by a network trained on a private 55-subject dataset, and the paper only states that the output 'was validated by our experts' with no quantitative metric. These synthetic WMn images are then used in §2.4 to train and apply a deep network for thalamic nuclei segmentation (based on THOMAS data). Any systematic synthesis artifact—intensity bias, over-smoothing, or hallucinated contrast in deep gray matter—will propagate directly into the nuclear boundaries and, after majority voting, into the atlas labels. This is particularly concerning for the small structures explicitly mentioned as error-prone (Mammillothalamic Tract, Habenular Nucleus). The authors must provide quantitative evidence (e.g., Dice agreement between synthetic WMn segmentation and real WMn segmentation, or comparison against manual labels) or substantially soften the claims about WMn-dependent substructure accuracy.
  3. [§3; §4] No quantitative evaluation of the atlas labels is provided. The paper states in §4 that 'the inclusion of human expertise ensures the accuracy and reliability of the final atlas' and in the limitations paragraph that 'the fact that it is based on the fusion of automatic segmentations may raise doubts about its accuracy,' but no overlap measures, volume comparisons with independent manual segmentations, or inter-rater statistics are reported. Since the atlas is proposed as a reference, the absence of any quantitative label validation is a load-bearing gap. At minimum, the authors should report overlap of the final atlas labels with one or more independent manual delineations (even on a subset of structures) or clearly state that label accuracy rests solely on expert visual QC.
minor comments (5)
  1. [Abstract; throughout] The phrase '0.125 mm3 resolution' appears repeatedly; since mm^3 is a volume unit, this should be '0.125 mm^3 voxel volume' or '0.5 mm isotropic resolution' to avoid confusion.
  2. [§2.4, Hippocampus subfield integration] There is a typo: 'A throughout QC was done' should read 'A thorough QC was done.'
  3. [§1 and References] The reference list has several minor issues: 'Mazziota' should be 'Mazziotta' in the Discussion; reference 38 is empty; and 'compressive list' should be 'comprehensive list' in §1.
  4. [§2.4, Thalamus subfield integration] The description of the 'intermediate space' label is vague: it is defined as 'the WM region connecting all the nuclei,' but it is not clear how this region was derived or whether it was manually edited. Please clarify the label definition and its QC process.
  5. [§2.5] The template construction mentions median-based sharpening and majority voting but does not specify the number of subjects contributing to each voxel or how missing labels (e.g., structures not present in all subjects) were handled. A sentence on the coverage or left/right symmetry of the labels would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: holiAtlas is a construction artifact; synthetic WMn and fused labels are processing inputs, and the acknowledged accuracy limitations are external validation concerns, not circular derivations.

full rationale

The atlas is built by averaging images and segmentations of 75 subjects and fusing the outputs of seven segmentation tools; the final labels are a majority-vote artifact, not a quantity predicted from those tool outputs. The only potentially fragile link is synthetic WMn in Section 2.2: a network trained on the private DeepMultiBrain dataset generates WMn for HCP subjects, with quality "validated by our experts" and no quantitative metric, and Section 2.4 uses these synthetic WMn images to train and apply a THOMAS-based network for thalamic nuclei. This is a validity and reproducibility limitation, and the paper itself acknowledges that "the fact that it is based on the fusion of automatic segmentations may raise doubts about its accuracy." However, it is not circular: the synthetic contrast is an input produced by a trained network from external training data, not a quantity derived from the atlas labels, and the THOMAS segmentation is an externally defined protocol. Author-developed tools such as vol2Brain, pBrain, HIPS, CERES, and DeepCERES are heavily cited, but they are used as segmentation engines whose outputs are inputs to the atlas; no load-bearing argument reduces to a self-citation, no uniqueness theorem is imported from the authors' prior work, and no equation defines an atlas label in terms of the atlas itself. The central claim is a constructed resource rather than a derived prediction, so the derivation chain is self-contained; the main concerns are external validation, synthetic modality equivalence, and generalizability, not circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 2 invented entities

The atlas construction rests on several fitted deep networks and unvalidated domain assumptions. The paper provides no quantitative validation of the final labels against manual segmentations or true high-resolution data, so the accuracy of the 350-label protocol is assumed rather than demonstrated.

free parameters (4)
  • WMn synthesis network weights = not reported
    Deep network trained on 55 private Bordeaux subjects to synthesize WMn from T1w/T2w; applied to HCP data, so the WMn atlas template and all WMn-dependent labels depend on these fitted weights.
  • Tissue correction UNET weights = not reported
    Trained on 12 manually corrected cases and applied to the remaining 63 subjects; used to fix gray matter/CSF misclassification before structure relabeling.
  • Thalamus segmentation network weights = not reported
    Trained on 20 THOMAS WMn subjects; used to segment 12 thalamic nuclei on synthetic WMn images; the atlas's 13 per-thalamus labels depend on this fitted model.
  • Spatial-intensity diffusion parameters (Gaussian kernel width, MAP priors) = unspecified
    Used during label integration to relabel conflict voxels; values not reported.
assumptions (4)
  • domain assumption Synthetic WMn generated by the trained network is a valid substitute for real WMn in atlas construction.
    The WMn template and thalamic nuclei labels are based on synthesized contrast; no quantitative validation against real WMn is provided (Sections 2.2, 2.4).
  • domain assumption Each of the seven input segmentation protocols is accurate enough for its target structures, and their fusion yields anatomically correct labels.
    The atlas labels are built from automatic tools and manual correction; no independent manual reference is used to measure label accuracy (Sections 2.3-2.4, Discussion).
  • standard math Majority voting over 75 registered label maps produces an unbiased atlas label.
    Assumed standard label fusion; not formally justified in the paper (Section 2.5).
  • standard math ICBM 2009b template is an appropriate initial reference and groupwise registration converges to a representative average.
    Standard template construction assumption (Section 2.5).
invented entities (2)
  • Intermediate space (thalamus)
    purpose: A label added to fill white matter regions connecting the 12 THOMAS nuclei, making the thalamus fully labeled.
    Introduced by the authors to complete thalamus coverage; no independent anatomical validation (Section 2.4, Thalamus subfield integration).
  • Vessels + connective tissue
    purpose: Label created by 3D hole-filling of the combined foreground mask to make the atlas cover the whole intracranial cavity.
    No direct vessel/connective tissue imaging; the label is inferred from unfilled voxels (Section 2.4, Vessels and connective tissue).

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Cite this review

Pith. "Pith review of Ultra-high resolution multimodal MRI densely labelled holistic structural brain atlas." pith.science (2026). https://pith.science/paper/ESFPPQKP

@misc{pith2026250116879,
  author       = {Pith},
  title        = {Pith review of: Ultra-high resolution multimodal MRI densely labelled holistic structural brain atlas},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ESFPPQKP}},
  note         = {Machine review of arXiv:2501.16879}
}
abstract

In this paper, we introduce a novel structural holistic Atlas (holiAtlas) of the human brain anatomy based on multimodal and high-resolution MRI that covers several anatomical levels from the organ to the substructure level, using a new densely labelled protocol generated from the fusion of multiple local protocols at different scales. This atlas was constructed by averaging images and segmentations of 75 healthy subjects from the Human Connectome Project database. Specifically, MR images of T1, T2 and WMn (White Matter nulled) contrasts at 0.125 $mm^{3}$ resolution were selected for this project. The images of these 75 subjects were nonlinearly registered and averaged using symmetric group-wise normalisation to construct the atlas. At the finest level, the proposed atlas has 350 different labels derived from 7 distinct delineation protocols. These labels were grouped at multiple scales, offering a coherent and consistent holistic representation of the brain across different levels of detail. This multiscale and multimodal atlas can be used to develop new ultra-high-resolution segmentation methods, potentially improving the early detection of neurological disorders. We make it publicly available to the scientific community.

Figures

Figures reproduced from arXiv: 2501.16879 by the authors.

Figure 1
Figure 1. Example of a preprocessed T1w, T2w and the synthetic WMn images at 0.125 mm3 resolution MNI152 space [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 12
Figure 12. Left: Central area of the upsampled MNI152 atlas. Right: Central area of the proposed atlas. Note that the proposed atlas has a better image quality compared to classic MNI152 atlas (for example, thalamic nuclei can be better observed at the new atlas) [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.