Pith. sign in

REVIEW 3 major objections 5 minor 51 references

CINeMA: Conditional Implicit Neural Multi-Modal Atlas for a Spatio-Temporal Representation of the Perinatal Brain

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A single conditional implicit neural network replaces registration-based atlas pipelines, producing high-resolution spatio-temporal atlases of healthy and pathological fetal and neonatal brains in minutes from small cohorts.

desk verdict Useful extension of CINA with honest ablations, but the conditional pathology claim is not yet backed by strong evidence. read the letter →

arxiv 2506.09668 v1 pith:M4KUMBSZ submitted 2025-06-11 cs.CV cs.LG

Maik Dannecker , Vasiliki Sideri-Lampretsa , Sophie Starck , Angeline Mihailov , Mathieu Milh , Nadine Girard , Guillaume Auzias , Daniel Rueckert This is my paper
classification cs.CVcs.LG
keywords conditionalatlasperinatalbrainimagingimplicitneuralrepresentationspatio-temporalmagneticresonancetissuesegmentationventriculomegalyagenesisofthecorpuscallosum
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

Brain atlases are standard tools for studying how the fetal and neonatal brain grows, but conventional atlas pipelines require many age-balanced scans and days of deformable registration, which breaks down for rare pathologies where data are scarce. CINeMA, a conditional implicit neural multimodal atlas, claims to remove that bottleneck: one neural network is trained across a cohort, enrolling each subject's anatomy in a personal latent code, and any desired atlas—at any gestational age, resolution, or modality, and optionally conditioned on ventricle size or corpus-callosum status—is a weighted averaging of latent codes followed by a single forward pass. Training takes roughly 12 minutes and atlas inference about 3 seconds, and the same model performs tissue segmentation, scan-age and birth-age prediction, and cross-modal translation. The paper reports higher anatomical fidelity and segmentation accuracy than registration-based and GAN-based atlases, especially for severe ventriculomegaly, where the non-pathological baselines largely fail. If the central claim is correct, CINeMA offers a practical way to model atypical neurodevelopment from small cohorts and to generate synthetic but anatomically informed data.

What carries the argument

The load-bearing object is the auto-decoded, conditionally modulated implicit neural representation: per-subject spatial latent codes $z_i \in \mathbb{R}^{256 \times 3 \times 3 \times 3}$, queried by trilinear interpolation at each 3D coordinate, are concatenated with explicit condition variables $\xi$ and converted by a linear layer into scale-and-shift parameters that modulate sinusoidal (SIREN) MLP layers, while per-subject rigid rotations and translations $R_i$ align all brains into a single reference space during training. The shared weights $\theta$ of the MLP encode the population-level anatomy; the latent codes absorb subject-specific anatomy. To build an atlas of age $t$, the training latent codes are combined through a Gaussian kernel with width 0.5 weeks: $\bar{z}_t = \sum_i w(t,t_i) z_i$, and one forward pass through the modulated MLP outputs intensities and tissue probabilities in any requested modality and resolution. This architecture is what removes deformable registration, makes atlas construction a single forward pass, and enables conditioning and latent-space interpolation.

What would settle it

Take a trained CINeMA model, fix a subject's latent code, and regenerate the atlas while sweeping the explicit conditioning value from its minimum to its maximum; if the lateral-ventricle volume present in the generated segmentation maps does not change along the sweep, or if toggling the ACC label does not make the corpus callosum appear or disappear, then the explicit conditions are not controlling the generated anatomy and the pathological atlases are not condition-driven.

Watch

Extended reading notes

Core claim

CINeMA is an extension of the conditional implicit neural atlas idea. A single multilayer perceptron with sinusoidal activations is trained across a cohort of fetal or neonatal brain scans; the network's shared weights learn the anatomy common to the population, while each subject's anatomical individuality is pushed into a subject-specific spatial latent code, and a per-subject learnable rigid transformation handles gross misalignment. Because variability lives in low-dimensional codes rather than in deformation fields, atlas construction requires no image registration. At inference, a temporal atlas for any target postmenstrual age is produced by taking a Gaussian-weighted average of training latent codes around that age and running one forward pass through the network; explicit extra dimensions concatenated to the code allow the atlas to be conditioned on continuous or discrete anatomical characteristics such as lateral ventricle volume or the complete absence of the corpus callosum. The paper reports that this procedure yields higher anatomical fidelity, sharper tissue segmentations, and more accurate age prediction than registration-based atlases and a GAN-based conditional atlas, and that it does so with far less data and in minutes rather than days, including for pathological ventriculomegaly and agenesis of the corpus callosum populations.

Load-bearing premise

The entire conditional-atlas claim rests on the assumption that the network actually uses the explicit condition variables rather than ignoring them, a separation the authors themselves note is 'not strictly enforced.'

Editorial extensions

If this is right

  • Atlas construction for a new population would take about 12 minutes of training on a single GPU plus a roughly 3-second forward pass, instead of the days required by registration-based pipelines.
  • A user could generate a continuous atlas at any gestational age and spatial resolution, and condition it on lateral ventricle volume or corpus-callosum status, including values interpolated between, or extrapolated beyond, the training distribution.
  • For a new subject, adapting the atlas to the scan's T2w image alone yields tissue probability maps, scan-age and birth-age estimates, and translation to T1w (and vice versa) without external registration.
  • Tissue-volume growth trajectories and gyrification trends derived from CINeMA atlases match established normative curves and reproduce the known inverse relation between ventricle enlargement and cortical folding (reported as $r = -0.97$ on generated atlases).
  • On the reported cohorts, CINeMA reports higher PSNR/SSIM, higher Dice scores in atlas-based segmentation, and lower scan-age prediction error than all three baselines; the largest margin appears for severe ventriculomegaly fetuses.

Reading between the lines

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

  • Editorial: The same conditional mechanism could be used as an in-silico assay: generate atlases across a ventricle-volume sweep and compare the implied gyrification index or cortical-thickness trajectories with real longitudinal cohorts, testing the atlas as a causal model rather than a descriptive one.
  • Editorial: Because the network is trained without any hard constraint tying the condition dimensions to anatomy, a cheap diagnostic—randomly permuting condition labels across subjects and retraining—would reveal whether the reported pathological atlases are a genuine effect of conditioning or just population averages.
  • Editorial: If latent codes prove stable and compact, they could serve as a nuisance-removed phenotype for group studies, letting researchers compare preterm versus term populations directly in latent space instead of in image space.
  • Editorial: The framework should extend to other rare malformations by stacking additional binary or discrete condition dimensions, but the paper only demonstrates two pathologies in isolation; combined multi-pathology conditioning, which is untested, would show whether the conditioning space scales.
Share X Bluesky LinkedIn Reddit HN

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 CINeMA, an auto-decoder implicit neural representation framework for building spatio-temporal, multimodal atlases of the perinatal brain. Subject-specific variability is encoded in spatial latent codes and learned rigid alignments, while explicit conditioning variables (gestational age, birth age, ventricle volume, corpus callosum status) are concatenated to the latent code. Atlas generation at a target time point is performed by a Gaussian-weighted average of training latent codes followed by a single forward pass. The authors report improved segmentation, scan-age prediction, and modality translation compared to registration-based and GAN-based baselines on dHCP and MarsFet data, and they demonstrate qualitative conditional atlases for ventriculomegaly and agenesis of the corpus callosum. The central claims are that CINeMA surpasses state-of-the-art accuracy, operates in low-data regimes, and enables flexible, disentangled conditioning on pathologies.

Significance. If established, CINeMA would be a practically valuable alternative to registration-based atlas pipelines: it requires no deformable registration, builds an atlas in minutes, supports multiple modalities, and can be adapted to individual subjects in seconds. The paper provides a public code release, evaluates on multiple datasets including pathological cases, and includes a thorough ablation study of architectural choices (multimodal input, rigid alignment, spatial latent codes, explicit conditioning). The runtime advantages and the breadth of downstream tasks are genuine strengths. However, the most novel component—conditional generation of pathological atlases—is not quantitatively validated, and the paper itself concedes that the explicit condition is not enforced to be disentangled. The global accuracy claim is also stronger than the evidence in Table I, which shows a PSNR deficit on term neonates relative to Atlas-GAN. These issues are central to the paper's contribution and need to be addressed before the claims can be accepted.

major comments (3)
  1. [Abstract, Section VII, Table I] The abstract states that CINeMA surpasses state-of-the-art methods 'in accuracy,' and the conclusion claims 'higher anatomical fidelity and more accurate representations than established baselines.' Table I does not fully support this. On dHCP term neonates, Atlas-GAN achieves PSNR 23.75±0.88 versus CINeMA's 23.43±0.55, and only SSIM and DSC are marked as significantly better for CINeMA. The accuracy advantage is thus metric- and cohort-dependent. The authors should either qualify the claim or explain the PSNR trade-off, e.g., by reporting a combined evaluation or discussing why higher structural similarity does not translate to pixel-wise fidelity.
  2. [Section III-D, Section V-B.2, Section V-B.3, Section VI-A] The conditional pathology atlas is the central novel contribution, but the manuscript does not demonstrate that the explicit condition ξ actually controls the generated anatomy. For ventriculomegaly, the only quantitative evidence is Fig. 3b, which reports a correlation (r=-0.97) between the conditioned LV volume and the gyrification index of the generated atlas. This is a model output, not a causal test: the same trend could be produced by age- or subject-correlated structure in the latent code z even if ξ were ignored. For ACC, Section V-B.3 provides only qualitative visual inspection, with no metric such as corpus callosum volume, overlap, or a classification accuracy on generated images. Section VI-A explicitly concedes that 'this separation is not strictly enforced, theoretically allowing the network to ignore explicit conditions.' To support the conditioning claim, the authors should provide direct quantitative tests, such as measuring LV volume in generated atlases as a function of ξ and comparing to target values, decoding the condition from generated images, or ablating ξ during training (e.g., training with shuffled condition labels and showing that conditioning performance degrades).
  3. [Section V-A, Table I (MarsFet severe ventriculomegaly row)] The text in Section V-A states that CINeMA demonstrates 'more accurate tissue segmentation throughout all datasets,' but for the severe ventriculomegaly cohort the DSC metric is reported only for the lateral ventricles (dagger footnote), not for the full tissue classes used in the other cohorts. This overstates the pathological segmentation result. The authors should either report multi-class DSC for this cohort or explicitly state that the evaluation is limited to the lateral ventricles in the main text, not only in a table footnote.
minor comments (5)
  1. [Section III-B, Eq. (3)] The Gaussian kernel standard deviation is given as σ_b=0.5 weeks, but the training ages t_i are said to be normalized to [-1,1]. Please clarify whether t in Eq. (3) is expressed in weeks or in the normalized coordinate system, and report a sensitivity analysis for the choice of σ_b, especially given the admitted limitations for unbalanced data in Section VI-A.
  2. [Table II] The notation 'z 256×3^3' and the CNN kernel-size entries are difficult to parse, and the 'shaded row' indicating the final configuration may not be visible in black-and-white printing. Please make the configuration indicator explicit, e.g., with a row label or a footnote.
  3. [Section V-B.2] The text says the LV volume 'ξ' is added as a 'disjoint, i.e., static, dimension,' but Section III-C describes ξ as learnable during test-time adaptation. Please clarify what 'static' means during training versus inference.
  4. [Section VI-A] There is a typo: 'primising' should be 'promising' in the discussion of spatial transformer networks.
  5. [Section V-A] The note that CINeMA was not explicitly conditioned on LV volume in the segmentation experiments and that explicit conditioning gave no benefit is important context; it should be moved to or repeated in the limitations discussion so that readers do not conflate the segmentation results with the conditional generation results.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: CINeMA's atlas construction and evaluations are self-contained; the admitted non-enforcement of conditioning is a validity caveat, not a circular reduction.

full rationale

Reviewing the derivation chain, CINeMA's training objective (Eq. 1) is a standard auto-decoder reconstruction loss over latent codes, alignment parameters, and network weights; atlas inference (Eqs. 3-4) is defined as a Gaussian-kernel-weighted latent-code combination followed by a forward pass, so temporal smoothness is a design choice rather than a predicted emergent property. The segmentation, age-estimation, and conditioning results are evaluated on held-out subjects and against external baselines (Table I, Figs. 2-4); the age estimates are obtained by test-time latent/condition optimization without using the target label in the loss, so they are fitted latent variables rather than circularly re-imported outputs. The only potentially load-bearing assumption—explicit-condition disentanglement—is explicitly conceded in Section VI-A as 'not strictly enforced, theoretically allowing the network to ignore explicit conditions,' and Section V-A notes that explicit conditioning provided no benefit in the segmentation experiments; this is a validity/interpretability limitation of the conditioning claim, not a circular reduction of a prediction to its input. Self-citations to the authors' prior CINA [14] describe the architectural lineage and are not used to justify the central empirical results. Hence no significant circularity.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The method is an empirical machine learning system; it introduces no derivation. Its central claims rest on several modeling assumptions about latent codes, age encoding, and conditioning disentanglement, and its behavior depends on many hand-set hyperparameters. No new physical entities are posited; the latent codes and condition variables are optimization parameters.

free parameters (6)
  • Latent code dimensions = D=256, X=3x3x3
    Selected by validation; Table II shows a trade-off between segmentation accuracy and age prediction, so the central representation capacity depends on this choice.
  • Gaussian kernel width sigma_b for atlas inference = 0.5 weeks (subjects within +/-1 week contribute roughly 95% weight)
    Set empirically in Equation (3); controls atlas sharpness versus subject bias.
  • INR architecture = 5 hidden layers x 1024 units, modulation on layers 1, 3, 5
    Hyperparameters optimized on a validation set, not derived.
  • Optimization hyperparameters = lr_INR=1e-4, lr_z=5e-4, lr_R=7.5e-3, batch=25k coordinates, 1 epoch
    Training schedule and learning rates chosen by hand; atlas quality depends on them.
  • Explicit condition encoding = LV volume normalized by total brain volume and rescaled to [-1,1]; ACC encoded as -1/+1
    Hand-chosen normalization for conditioning variables in Section III-D.
  • Test-time adaptation schedule = 10 epochs, early stopping on 10% holdout
    Stopping criterion and epoch count are specified but not justified; they influence age prediction and segmentation quality.
assumptions (5)
  • domain assumption An auto-decoder INR with per-subject latent codes and learned rigid alignment can represent perinatal brain anatomy without deformable registration.
    Section III-A; the entire no-registration claim rests on this. It is supported empirically by Table I but not proven.
  • domain assumption The first principal component of latent codes tracks postmenstrual age, making Gaussian kernel-weighted averaging of latent codes a valid spatio-temporal atlas.
    Section III-B and Figure 1-B; no quantitative correlation is reported.
  • domain assumption Explicit conditioning dimensions are approximately disentangled from anatomy.
    Section III-D and VI-A; the paper admits the separation is not enforced and the network could ignore explicit conditions.
  • ad hoc to paper A fixed Gaussian kernel with sigma_b=0.5 weeks is appropriate for all ages and condition combinations.
    Section III-B; the Discussion in Section VI-A itself suggests adaptive kernels may be needed for unbalanced data.
  • domain assumption MarsFet segmentations produced by a dHCP-trained nnU-Net with manual corrections are accurate enough to serve as ground truth for training and evaluation.
    Section IV-A.3; only five fetuses with severe ventriculomegaly are manually corrected.

how reviews work

0 comments
Cite this review

Pith. "Pith review of CINeMA: Conditional Implicit Neural Multi-Modal Atlas for a Spatio-Temporal Representation of the Perinatal Brain." pith.science (2026). https://pith.science/paper/M4KUMBSZ

@misc{pith2026250609668,
  author       = {Pith},
  title        = {Pith review of: CINeMA: Conditional Implicit Neural Multi-Modal Atlas for a Spatio-Temporal Representation of the Perinatal Brain},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M4KUMBSZ}},
  note         = {Machine review of arXiv:2506.09668}
}
read the original abstract

Magnetic resonance imaging of fetal and neonatal brains reveals rapid neurodevelopment marked by substantial anatomical changes unfolding within days. Studying this critical stage of the developing human brain, therefore, requires accurate brain models-referred to as atlases-of high spatial and temporal resolution. To meet these demands, established traditional atlases and recently proposed deep learning-based methods rely on large and comprehensive datasets. This poses a major challenge for studying brains in the presence of pathologies for which data remains scarce. We address this limitation with CINeMA (Conditional Implicit Neural Multi-Modal Atlas), a novel framework for creating high-resolution, spatio-temporal, multimodal brain atlases, suitable for low-data settings. Unlike established methods, CINeMA operates in latent space, avoiding compute-intensive image registration and reducing atlas construction times from days to minutes. Furthermore, it enables flexible conditioning on anatomical features including GA, birth age, and pathologies like ventriculomegaly (VM) and agenesis of the corpus callosum (ACC). CINeMA supports downstream tasks such as tissue segmentation and age prediction whereas its generative properties enable synthetic data creation and anatomically informed data augmentation. Surpassing state-of-the-art methods in accuracy, efficiency, and versatility, CINeMA represents a powerful tool for advancing brain research. We release the code and atlases at https://github.com/m-dannecker/CINeMA.

Figures

Figures reproduced from arXiv: 2506.09668 by the authors.

Figure 1
Figure 1. Overview of atlas construction with CINeMA. (i) Training is conducted on N subjects, each represented by image modalities, tissue segmentation maps, and randomly initialized latent code (z) and alignment parameter (R). The implicit neural representation (INR) predicts intensities and tissue probabilities for sampled coordinates, encoding subject-specific features in (z) and general features in the shared weights (θ)… view at source ↗
Figure 2
Figure 2. (Left) Multimodal neonatal atlas for 37-44 weeks PMA generated with CINeMA, depicting T1w, T2w, and cGM probability maps. (Right) Tissue growth and cortical maturation for atlases from CINeMA (purple) vs. baselines Deepali (red), BDAtlas (blue), and Atlas-GAN (green). Black: normative mean with ±1–3 SDs computed with [39]. Gyrification index (GI) defined as GI = WMsurface WMexposed . Note: Atlas-GAN lacks volume ana… view at source ↗
Figure 3
Figure 3. a) Conditional atlases for varying lateral ventricle (LV) volumes using CINeMA (left) and Atlas-GAN (right). Intensity and tissue maps for CSF and LV show enlarged (top), normal (middle), and collapsed (bottom) ventricles. Note: Atlas-GAN atlases are size-invariant due to affine pre-alignment. b) White matter (WM) surface area, WM exposed surface area, and gyrification index (GI = WMsurface WMexposed ) of CIN￾eMA at… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: (Top) Atlases fitted to a new subject with severely enlarged LV (yellow label). All baselines were fitted using deformable registration [3]. Atlas-GAN was conditioned on maximum possible LV size. CINeMA was fitted via latent code optimization (see Section III-C). (Bott…
Figure 6
Figure 6. Figure 6: Fetal brain atlas at 27 weeks GA, conditioned on ACC with corresponding white matter (WM) probability maps, generated by CINeMA. Axial (left) and sagittal (right) views show a normal CC (left), complete ACC (right), and an interpolated state resembling CC hypoplasia (m…
Figure 7
Figure 7. Figure 7: Translation/prediction to/of unseen modalities during test-time adaption on new subjects. (left) Atlas is adapted to a new T2w scan, with translation to T1w. (right) Adapted to T1w scan with translation to T2w. C. Multi-Modal Atlas CINeMA supports both mono and multimo…
Figure 9
Figure 9. Figure 9: Image reconstruction of latent codes interpolated between two distant subjects (22 weeks and 31 weeks GA). attributes, further expanding its generative potential. The fol￾lowing sections explore this in detail. 1) Modulation of Specific Anatomical Features: Explicit co…
Figure 10
Figure 10. Figure 10: Neonatal atlas at 42 weeks PMA generated with CINeMA at four resolutions (2.0–0.3mm3) using the same model. Anatomical con￾sistency is preserved up to partial volume effects. Memory footprints of full temporal atlas (37-45 weeks): 1.0mm3 ~40MB, 0.3mm3 ~1.2GB, all mode…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

51 extracted references · 46 canonical work pages

  1. [1]

    A fast diffeomorphic image registration algorithm,

    J. Ashburner, “A fast diffeomorphic image registration algorithm,” Neuroimage, vol. 38, no. 1, pp. 95–113, 2007

    work page 2007

  2. [2]

    Freesurfer,

    B. Fischl, “Freesurfer,”Neuroimage, vol. 62, no. 2, pp. 774–781, 2012

    work page 2012

  3. [3]

    Advanced normalization tools (ants),

    B. B. Avants, N. Tustison, and G. Song, “Advanced normalization tools (ants),”Insight j, vol. 2, no. 365, pp. 1–35, 2009

    work page 2009

  4. [4]

    Bounti: Brain volumetry and automated parcellation for 3d fetal mri,

    A. U. Uuset al., “Bounti: Brain volumetry and automated parcellation for 3d fetal mri,”bioRxiv, 2023

    work page 2023

  5. [5]

    Fetal brain mri atlases and datasets: A review,

    T. Ciceriet al., “Fetal brain mri atlases and datasets: A review,” NeuroImage, vol. 292, p. 120603, 2024

    work page 2024

  6. [6]

    Construction of a consistent high-definition spatio- temporal atlas of the developing brain using adaptive kernel regression,

    A. Seraget al., “Construction of a consistent high-definition spatio- temporal atlas of the developing brain using adaptive kernel regression,” Neuroimage, vol. 59, no. 3, pp. 2255–65, 2012. 12 THIS WORK HAS BEEN SUBMITTED TO THE IEEE FOR POSSIBLE PUBLICATION

    work page 2012

  7. [7]

    A normative spatiotemporal mri atlas of the fetal brain for automatic segmentation and analysis of early brain growth,

    A. Gholipouret al., “A normative spatiotemporal mri atlas of the fetal brain for automatic segmentation and analysis of early brain growth,” Sci Rep, vol. 7, no. 1, p. 476, 2017

    work page 2017

  8. [8]

    An automatic multi-tissue human fetal brain segmen- tation benchmark using the fetal tissue annotation dataset,

    K. Payetteet al., “An automatic multi-tissue human fetal brain segmen- tation benchmark using the fetal tissue annotation dataset,”Scientific Data, vol. 8, no. 1, p. 167, 2021

    work page 2021

Show all 51 references
  1. [9]

    The developing human connectome project (dhcp): fetal acquisition protocol,

    A. N. Priceet al., “The developing human connectome project (dhcp): fetal acquisition protocol,” inProceedings of the annual meeting of the ISMRM, vol. 244, 2019

    2019

  2. [10]

    The developing human connectome project neonatal data release,

    A. D. Edwardset al., “The developing human connectome project neonatal data release,”Frontiers in Neuroscience, vol. 16, 2022

    2022

  3. [11]

    Generative adversarial reg- istration for improved conditional deformable templates,

    N. Dey, M. Ren, A. V . Dalca, and G. Gerig, “Generative adversarial reg- istration for improved conditional deformable templates,” inProceedings of the IEEE/CVF international conference on computer vision, pp. 3929– 3941, 2021

    2021

  4. [12]

    Learning conditional deformable templates with convolutional networks,

    A. Dalca, M. Rakic, J. Guttag, and M. Sabuncu, “Learning conditional deformable templates with convolutional networks,”Advances in neural information processing systems, vol. 32, 2019

    2019

  5. [13]

    Atlas-istn: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks,

    M. Sinclairet al., “Atlas-istn: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks,”Medical Image Analysis, vol. 78, p. 102383, 2022

    2022

  6. [14]

    Cina: Conditional implicit neural atlas for spatio-temporal representation of fetal brains,

    M. Dannecker, V . Kyriakopoulou, L. Cordero-Grande, A. N. Price, J. V . Hajnal, and D. Rueckert, “Cina: Conditional implicit neural atlas for spatio-temporal representation of fetal brains,” inMedical Image Computing and Computer Assisted Intervention – MICCAI 2024, pp. 181–191, 2024

    2024

  7. [15]

    3d statistical neuroanatomical models from 305 mri volumes,

    A. C. Evans, D. L. Collins, S. Mills, E. D. Brown, R. L. Kelly, and T. M. Peters, “3d statistical neuroanatomical models from 305 mri volumes,” in1993 IEEE conference record nuclear science symposium and medical imaging conference, pp. 1813–1817, 1993

    1993

  8. [16]

    A probabilistic atlas and reference system for the human brain: International consortium for brain mapping (icbm),

    J. Mazziottaet al., “A probabilistic atlas and reference system for the human brain: International consortium for brain mapping (icbm),” Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, vol. 356, no. 1412, pp. 1293–1322, 2001

    2001

  9. [17]

    3-dimensional proportional system: an approach to cerebral imaging,

    J. Talairach, “3-dimensional proportional system: an approach to cerebral imaging,”Co-planar stereotaxic atlas of the human brain, 1988

    1988

  10. [18]

    Consistent groupwise non-rigid registration for atlas construction,

    K. K. Bhatia, J. V . Hajnal, B. K. Puri, A. D. Edwards, and D. Rueckert, “Consistent groupwise non-rigid registration for atlas construction,” in2004 2nd IEEE International Symposium on Biomedical Imaging, pp. 908–911, 2004

    2004

  11. [19]

    A spatiotemporal atlas of mr intensity, tissue prob- ability and shape of the fetal brain with application to segmentation,

    P. A. Habaset al., “A spatiotemporal atlas of mr intensity, tissue prob- ability and shape of the fetal brain with application to segmentation,” Neuroimage, vol. 53, no. 2, pp. 460–470, 2010

    2010

  12. [20]

    A dynamic 4d probabilistic atlas of the developing brain,

    M. Kuklisova-Murgasovaet al., “A dynamic 4d probabilistic atlas of the developing brain,”Neuroimage, vol. 54, no. 4, pp. 2750–63, 2011

    2011

  13. [21]

    Regional growth and atlasing of the developing human brain,

    A. Makropouloset al., “Regional growth and atlasing of the developing human brain,”Neuroimage, vol. 125, pp. 456–478, 2016

    2016

  14. [22]

    Nonrigid registration using free-form deformations: application to breast mr images,

    D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: application to breast mr images,”IEEE transactions on medical imaging, vol. 18, no. 8, pp. 712–721, 1999

    1999

  15. [23]

    Spatial transformer networks,

    M. Jaderberget al., “Spatial transformer networks,”Advances in neural information processing systems, vol. 28, 2015

    2015

  16. [24]

    V oxelmorph: a learning framework for deformable medical image registration,

    G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, and A. V . Dalca, “V oxelmorph: a learning framework for deformable medical image registration,”IEEE transactions on medical imaging, vol. 38, no. 8, pp. 1788–1800, 2019

    2019

  17. [25]

    A deep learning framework for unsupervised affine and deformable image registration,

    B. D. De V os, F. F. Berendsen, M. A. Viergever, H. Sokooti, M. Staring, and I. I ˇsgum, “A deep learning framework for unsupervised affine and deformable image registration,”Medical image analysis, vol. 52, pp. 128–143, 2019

    2019

  18. [26]

    Aladdin: Joint atlas building and diffeo- morphic registration learning with pairwise alignment,

    Z. Ding and M. Niethammer, “Aladdin: Joint atlas building and diffeo- morphic registration learning with pairwise alignment,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion (CVPR), pp. 20784–20793, 2022

    2022

  19. [27]

    Cas-net: Conditional atlas generation and brain segmenta- tion for fetal mri,

    L. Liet al., “Cas-net: Conditional atlas generation and brain segmenta- tion for fetal mri,” inUncertainty for Safe Utilization of Machine Learn- ing in Medical Imaging, and Perinatal Imaging (MICCAI Workshops), pp. 221–230, 2021

    2021

  20. [28]

    Nerf: Representing scenes as neural radiance fields for view synthesis,

    B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,”Communications of the ACM, vol. 65, no. 1, pp. 99–106, 2021

    2021

  21. [29]

    Implicit neural representations with periodic activation functions,

    V . Sitzmann, J. Martel, A. Bergman, D. Lindell, and G. Wetzstein, “Implicit neural representations with periodic activation functions,” Advances in neural information processing systems, vol. 33, pp. 7462– 7473, 2020

    2020

  22. [30]

    Deepsdf: Learning continuous signed distance functions for shape rep- resentation,

    J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “Deepsdf: Learning continuous signed distance functions for shape rep- resentation,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 165–174, 2019

    2019

  23. [31]

    Modulated periodic activations for generalizable local functional representations,

    I. Mehta, M. Gharbi, C. Barnes, E. Shechtman, R. Ramamoorthi, and M. Chandraker, “Modulated periodic activations for generalizable local functional representations,” inProceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pp. 14214–14223, 2021

    2021

  24. [32]

    From data to functa: Your data point is a function and you can treat it like one,

    E. Dupont, H. Kim, S. M. A. Eslami, D. J. Rezende, and D. Rosenbaum, “From data to functa: Your data point is a function and you can treat it like one,” in39th International Conference on Machine Learning (ICML), 2022

    2022

  25. [33]

    Scene representation networks: Continuous 3d-structure-aware neural scene representations,

    V . Sitzmann, M. Zollh ¨ofer, and G. Wetzstein, “Scene representation networks: Continuous 3d-structure-aware neural scene representations,” Advances in Neural Information Processing Systems, vol. 32, 2019

    2019

  26. [34]

    Continuous longitudinal fetus brain atlas construction via implicit neural representation,

    L. Chen, J. Wu, Q. Wu, H. Wei, and Y . Zhang, “Continuous longitudinal fetus brain atlas construction via implicit neural representation,” in Perinatal, Preterm and Paediatric Image Analysis (MICCAI Workshop), pp. 38–47, 2022

    2022

  27. [35]

    Sina: Sharp implicit neural atlases by joint optimisation of representation and deformation,

    C. Großbr ¨ohmer, Z. A.-H. Hemidi, F. Falta, and M. P. Heinrich, “Sina: Sharp implicit neural atlases by joint optimisation of representation and deformation,” inBiomedical Image Registration, pp. 165–180, 2024

    2024

  28. [36]

    Metasdf: Meta-learning signed distance functions,

    V . Sitzmann, E. Chan, R. Tucker, N. Snavely, and G. Wetzstein, “Metasdf: Meta-learning signed distance functions,” inNeurIPS, vol. 33, pp. 10136–10147, 2020

    2020

  29. [37]

    Spatial functa: Scaling functa to imagenet classification and generation,

    M. Bauer, E. Dupont, A. Brock, D. Rosenbaum, J. R. Schwarz, and H. Kim, “Spatial functa: Scaling functa to imagenet classification and generation,”arXiv preprint arXiv:2302.03130, 2023

    2023 arXiv

  30. [38]

    Instant neural graphics primitives with a multiresolution hash encoding,

    T. M ¨uller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,”ACM Transactions on Graphics (ToG), vol. 41, no. 4, pp. 1–15, 2022

    2022

  31. [39]

    Phenotyping the preterm brain: Characterizing individual deviations from normative volumetric development in two large infant cohorts,

    R. Dimitrovaet al., “Phenotyping the preterm brain: Characterizing individual deviations from normative volumetric development in two large infant cohorts,”Cerebral Cortex, vol. 31, pp. 3665–3677, 04 2021

    2021

  32. [40]

    The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruc- tion,

    A. Makropouloset al., “The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruc- tion,”Neuroimage, vol. 173, pp. 88–112, 2018

    2018

  33. [41]

    Automating motion compensation in 3t fetal brain imaging: localize, align and reconstruct,

    L. Cordero-Grande, A. Price, E. Hughes, R. Wright, M. Rutherford, and J. V . Hajnal, “Automating motion compensation in 3t fetal brain imaging: localize, align and reconstruct,” inISMRM, vol. 2019, p. 1000, 2019

    2019

  34. [42]

    Nesvor: implicit neural representation for slice-to-volume reconstruction in mri,

    J. Xuet al., “Nesvor: implicit neural representation for slice-to-volume reconstruction in mri,”IEEE transactions on medical imaging, vol. 42, no. 6, pp. 1707–1719, 2023

    2023

  35. [43]

    Burst of gyrification in the human brain after birth,

    A. Mihailovet al., “Burst of gyrification in the human brain after birth,” Communications Biology, vol. 8, no. 1, pp. 1–13, 2025

    2025

  36. [44]

    nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

    F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,”Nature methods, vol. 18, no. 2, pp. 203–211, 2021

    2021

  37. [45]

    deepali: Image, point set, and surface registration in pytorch (0.6.3),

    A. Schuh, H. Qiu, and H. Research, “deepali: Image, point set, and surface registration in pytorch (0.6.3),” 2024

    2024

  38. [46]

    Using uk biobank data to establish population-specific atlases from whole body mri,

    S. Starcket al., “Using uk biobank data to establish population-specific atlases from whole body mri,”Communications Medicine, vol. 4, no. 1, p. 237, 2024

    2024

  39. [47]

    Third trimester brain growth in preterm infants compared with in utero healthy fetuses,

    M. Bouyssi-Kobaret al., “Third trimester brain growth in preterm infants compared with in utero healthy fetuses,”Pediatrics, vol. 138, no. 5, 2016

    2016

  40. [48]

    Predicting age and clinical risk from the neonatal connectome,

    Y . Taoudi-Benchekrounet al., “Predicting age and clinical risk from the neonatal connectome,”NeuroImage, vol. 257, p. 119319, 2022

    2022

  41. [49]

    V olume growth trend and correlation of atrial diameter with lateral ventricular volume in normal fetus and fetus with ventriculomegaly: A strobe compliant article,

    H.-L. Ma, S.-X. Zhao, F.-R. Lv, Z.-W. Zhang, Y .-H. Xiao, and B. Sheng, “V olume growth trend and correlation of atrial diameter with lateral ventricular volume in normal fetus and fetus with ventriculomegaly: A strobe compliant article,”Medicine, vol. 98, no. 26, 2019

    2019

  42. [50]

    Cortical folding alterations in fetuses with iso- lated non-severe ventriculomegaly,

    O. M. Benkarimet al., “Cortical folding alterations in fetuses with iso- lated non-severe ventriculomegaly,”Neuroimage Clin, vol. 18, pp. 103– 114, 2018

    2018

  43. [51]

    Neural orientation distribution fields for estimation and uncertainty quantification in diffusion mri,

    W. Consagra, L. Ning, and Y . Rathi, “Neural orientation distribution fields for estimation and uncertainty quantification in diffusion mri,” Medical Image Analysis, vol. 93, p. 103105, 2024

    2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.