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REVIEW 4 major objections 5 minor 88 references

DeepSPV: A Deep Learning Pipeline for 3D Spleen Volume Estimation from 2D Ultrasound Images

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read DeepSPV estimates spleen volume from one or two 2D ultrasound images, and the best model beats human expert accuracy on synthetic ultrasound.

desk verdict Useful first system for spleen volume from 2D US, but the clinical-level accuracy claims rest on synthetic images that are easier to segment than real US, so the headline numbers are optimistic. read the letter →

arxiv 2411.11190 v2 pith:EE4FC7ZB submitted 2024-11-17 eess.IV cs.CV

Zhen Yuan , David Stojanovski , Lei Li , Alberto Gomez , Haran Jogeesvaran , Esther Puyol-Antón , Baba Inusa , Andrew P. King This is my paper · ORCID
classification eess.IVcs.CV
keywords splenomegaly2Dultrasounddeeplearningvolumeestimationvariationalautoencoderdiffusionmodelsyntheticsicklecelldisease
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

Spleen volume is the gold-standard measure of spleen size, but it normally requires CT or MRI, which are often unavailable where sickle cell disease is common. This paper argues that a deep learning pipeline called DeepSPV can recover 3D spleen volume from one coronal 2D ultrasound image, or from coronal plus transverse views, using only the segmentations visible in standard clinical images. On CT-derived segmentations, the best model reaches 86.62% mean relative volume accuracy with a single view and 92.58% with two views, compared with 68.54% and 81.60% for human experts using standard linear-regression formulas. On a full pipeline test that first segments synthetic ultrasound images generated by a diffusion model, then estimates volume, it reaches 83.0% from one view. If these numbers transfer to real patients, spleen volume could become a practical biomarker in routine ultrasound settings.

What carries the argument

The load-bearing object is the variational autoencoder (VAE) latent space built from spleen segmentations. A residual-block convolutional encoder maps a single coronal slice, or channel-wise concatenated coronal and transverse slices, to a 128-dimensional Gaussian latent code parameterised by mean and standard deviation; the decoder reconstructs the segmentation. Three volume estimators were tested on this code: nearest-neighbour search, linear regression on the latent mean, and an end-to-end regression VAE (RVAE) whose fully connected layers map the latent code to volume under a combined reconstruction/KL/regression loss. The RVAE is the variant that carries the reported accuracy, and its reparameterised sampling also generates confidence intervals. A second mechanism, the ultrasound semantic diffusion model (USDM), synthesises realistic ultrasound images from layouts made by rotating CT spleen segmentations and overlaying them on real ultrasound cones, providing the volume-labelled images needed to evaluate the full pipeline.

What would settle it

Collect paired 2D ultrasound images and CT or MRI spleen volumes from the same patients, run DeepSPV on the real ultrasound images, and compare mean relative volume accuracy with the CT volumes; if the accuracy falls substantially below the reported 83% or below the human-expert baseline, the synthetic-to-real transfer assumption fails.

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Extended reading notes

Core claim

The paper's central claim is that spleen volume is recoverable from the shape of the spleen as seen in standard 2D ultrasound views, without reconstructing a 3D volume. The authors train a residual-block convolutional variational autoencoder on 2D slices taken from 149 CT spleen segmentations, using the coronal slice with the largest cross-sectional area for the single-view setting and adding the corresponding transverse slice for the dual-view setting. Volume is read out from the 128-dimensional latent code; the best-performing variant, RVAE, adds a fully connected regression head to the latent space and trains it end-to-end with reconstruction, KL, and volume regression losses. The same framework can sample the latent distribution 100 times to produce 95% confidence intervals. To test the whole pipeline from ultrasound rather than from clean segmentations, the authors generate synthetic ultrasound images from CT-derived segmentations with a semantic diffusion model conditioned on US layouts; a U-Net segments these images and the RVAE estimates volume, yielding an overall MRVA of 83.0%, above the human expert baseline of 68.54%.

Load-bearing premise

The full-pipeline accuracy of 83.0% assumes that synthetic ultrasound images made by rotating and manually placing CT spleen segmentations into real ultrasound cones are representative enough of real clinical 2D ultrasound for the measured volume accuracy to carry over to patients, and that the coronal CT slice with the largest cross-sectional area matches the clinically recorded coronal view; no paired real ultrasound with volumetric ground truth is used anywhere in the evaluation.

Editorial extensions

If this is right

  • Spleen volume could be measured in routine 2D ultrasound examinations, removing the need for CT or MRI when assessing splenomegaly in settings where 3D imaging is unavailable.
  • A single coronal view suffices for useful volume estimates (86.62% MRVA on CT-derived segmentations), and adding a transverse view improves accuracy to 92.58%.
  • Confidence intervals and latent-space visualisation give clinicians a way to judge and trust individual volume estimates rather than receiving a bare scalar.
  • The diffusion-generated synthetic ultrasound dataset with ground-truth volumes is released publicly, giving other groups paired image-volume data that did not exist before.
  • Because the pipeline is built from standard clinical views, it could be integrated into existing ultrasound workflows for spleen assessment without changing acquisition practice.

Reading between the lines

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

  • If paired real ultrasound and CT/MRI volumes become available, the same architecture would provide a direct test of whether the 83.0% synthetic-pipeline accuracy transfers to patients; the paper does not claim this transfer has been shown.
  • Because the VAE regresses volume from shape rather than pixel texture, the method may extend to other organs whose volume matters clinically but where 3D imaging is impractical, such as the liver or kidneys, although each organ would need its own latent space and validation.
  • The authors' adult-CT versus paediatric-US mismatch suggests age-stratified calibration may be needed before clinical deployment, since spleen size and growth norms differ across childhood.
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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

4 major / 5 minor

Summary. The paper proposes DeepSPV, a two-stage deep learning pipeline that estimates 3D spleen volume from single or dual 2D ultrasound images: a U-Net segments the spleen in each 2D ultrasound image, and a VAE-based regressor (RVAE) maps the resulting segmentation(s) to a volume estimate. Because no paired real ultrasound volumes are available, the authors also introduce an ultrasound semantic diffusion model (USDM) that synthesizes realistic 2D ultrasound images from CT-derived spleen segmentations, and they evaluate the full pipeline on these synthetic images. On CT-derived 2D segmentations, the RVAE achieves 86.62% and 92.58% mean relative volume accuracy (MRVA) for single-view and dual-view settings, outperforming Pix2Vox++, ResNet/VGG/DenseNet regressors, and clinical linear-regression formulas. On synthetic ultrasound images, the full pipeline achieves 83.0% MRVA, which the authors compare with a human-expert MRVA of 68.54% and use to claim that the pipeline surpasses human performance.

Significance. If validated on real clinical data, the CT-based volume-estimation experiments would be a solid contribution: the 5-fold stratified evaluation, the inclusion of multiple baselines, the pairwise significance tests, and the confidence-interval and interpretability analyses are strengths. The public synthetic ultrasound dataset is also a useful resource for the community. However, the central clinical claim rests on the full-pipeline evaluation on synthetic ultrasound, and that evaluation has two serious gaps: the synthetic test images are segmented much more accurately than real ultrasound images by the same U-Net, and the human-expert comparator is not measured on the synthetic test set. The paper therefore does not yet establish the claimed clinical-level accuracy from 2D ultrasound.

major comments (4)
  1. [§5.2.4, Table 5] The full-pipeline MRVA of 83.0% is computed on synthetic ultrasound images generated from the held-out CT spleen segmentations (Section 4.2.2, Table 1). The 'Human Experts' value of 68.54% in Table 5 is not a measurement on those synthetic images; it is the single-view human-expert MRVA from Table 2, obtained from manual linear-regression measurements on CT-derived slices. The comparison is therefore apples-to-oranges and does not support the claim that the full pipeline surpasses human experts on the same data. Please either re-measure human performance on the synthetic test images or explicitly label the Table 5 human-expert row as a CT-based reference and remove the 'surpasses human experts' claim for the full pipeline.
  2. [§5.2.3, Table 4 vs §5.2.4] The segmentation U-Net achieves Dice 0.9746 and HD 4.09 mm on synthetic ultrasound images generated from synthetic layouts, but only 0.9285 and 11.11 mm on the real ultrasound test set. The full-pipeline volume error is measured on the synthetic test images, so the reported 83.0% MRVA is an optimistic upper bound for real clinical images. The paper should either quantify how the observed segmentation gap affects volume estimation (e.g., by running the volume estimator on real-US segmentations with an alternative volume source) or explicitly state that the true MRVA on real ultrasound may be lower. This issue directly affects the paper's central quantitative claim.
  3. [§3.3 and §4.1.2] The synthetic layouts are created by rotating a 2D CT spleen segmentation 90 degrees anti-clockwise and manually overlaying it on a real ultrasound cone, with no validation that this reproduces the clinically recorded coronal view, which is aimed at the longest spleen length. In addition, the coronal slice selected from each CT volume is the slice with the largest cross-sectional area, and it is assumed to correspond to the clinical coronal ultrasound view. If this assumption fails, the synthetic test set does not represent the clinical acquisition protocol, further undermining the transferability of the 83.0% MRVA. Please provide evidence for the anatomical correspondence or temper the claims about clinical workflow integration.
  4. [Section 6, last paragraph] The Discussion acknowledges that the CT data are from adults while the ultrasound data are from paediatric SCD patients, and then asserts 'we do not expect this difference to have a significant impact on our results and conclusions' without supporting evidence. Since the volume estimator is trained exclusively on adult CT segmentations and the target population is paediatric, this is an unsupported assumption that affects the generalisability claim. Please either provide quantitative evidence (e.g., age-stratified sensitivity analysis or literature-based volume ranges) or explicitly present the adult/pediatric mismatch as an unresolved limitation rather than a benign feature.
minor comments (5)
  1. [Abstract and §5.2.1] The abstract reports '86.62%/92.5%' but Table 2 reports 92.58%; please make these values consistent.
  2. [§3.2.1] The reparameterisation equation uses the notation 'z=µ+ζ J σ' with an undefined symbol; replace it with a standard element-wise product, e.g., 'z = µ + ζ ⊙ σ'.
  3. [Fig. 1] The figure contains the typo 'valiation' instead of 'validation'.
  4. [Data Availability] The Data Availability statement says the synthetic dataset 'will be made publicly available on paper publication', while the abstract states 'We also make our synthetic spleen ultrasound dataset publicly available'; please align the two statements.
  5. [References] The reference list contains duplicate entries for Altini et al. (2022) and Holmström et al. (2022); please deduplicate.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the volume estimator is trained and tested on disjoint CT folds, and the synthetic full-pipeline test images derive from held-out CT segmentations, so the central MRVA numbers are not forced by construction.

full rationale

The central claim (MRVA 86.62%/92.58% from single/dual-view CT-derived segmentations, and 83.0% full pipeline on synthetic US) rests on supervised regression with an explicit 5-fold split. Table 1 and Section 4.2.1 show the RVAE is trained on 89/30 CT segmentations and evaluated on a 30-case hold-out fold; the same hold-out CT segmentations are used to generate synthetic test US images (Section 4.2.3), so the volume model does not see its test targets during training. The U-Net is trained on disjoint real/synthetic training images and evaluated on real and synthetic test sets, giving independent Dice/HD evidence. The observed drop from 86.62% to 83.00% when moving from ground-truth CT segmentations to predicted synthetic-US segmentations is an empirical result, not an identity. Self-citations (Yuan et al. 2020, 2022) are used only to justify the U-Net architecture and hyperparameter choices, which are then independently trained and benchmarked, so they are not load-bearing. The main concerns in the paper are external validity (synthetic versus real US, adult CT versus paediatric US), not circularity: the paper itself acknowledges 'a limitation of our work is that we were only able to generate synthetic 2D coronal US images' and states 'we do not expect this difference to have a significant impact' about the adult/pediatric mismatch. The reuse of the CT-based human-expert MRVA of 68.54% in Table 5 is an unmatched comparison, but it does not reduce any model prediction to its training input. No equation in the derivation chain is equivalent to its input by construction.

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

All numbers reported are empirical accuracies of trained networks; there is no first-principles derivation of spleen volume from image intensities. The pipeline relies on architectural hyperparameters selected by validation, several domain assumptions about view correspondence and synthetic-to-real transfer, and standard background results from VAE and diffusion model literature. No new physical entities are introduced.

free parameters (5)
  • KL divergence weight w1 = 0.2
    Selected by grid search over 0.1 to 0.5 on the validation set; balances reconstruction and regularization of the VAE latent space.
  • Regression loss weight w2 for RVAE = 0.2
    Selected by grid search over 0.1 to 0.5 on the validation set; controls how strongly volume supervision shapes the latent space.
  • Latent dimension D = 128
    Architecture choice for the VAE latent space; no ablation or justification beyond prior practice.
  • Learning rate = 0.001
    Grid searched over 0.0001, 0.001, and 0.01; used for all volume estimation models.
  • Diffusion steps and denoising runs for USDM = 1000 steps; 100,000 denoising processes
    Chosen for synthetic ultrasound generation; no sensitivity analysis is reported.
assumptions (5)
  • domain assumption The coronal 2D slice with the largest cross-sectional area from CT corresponds to the clinically recorded coronal US view used for spleen length.
    Volume models train on max-area slices, Section 4.1.2, but clinical US images were recorded for longest length, Section 4.1.1. No validation that these are the same plane.
  • domain assumption Synthetic ultrasound images generated by USDM from CT segmentations and real US cones are representative of real clinical ultrasound images.
    The full pipeline is evaluated only on such synthetic images, Section 5.2.4, and the discussion concedes the domain gap may not be fully eliminated.
  • domain assumption Adult CT spleen shape statistics transfer to pediatric sickle cell disease patients.
    CT data are from adults, US data from pediatric SCD patients; the paper asserts no significant impact but notes future age-stratified validation is needed, Section 6.
  • ad hoc to paper The 90-degree rotation and manual overlay of CT spleen segmentations onto US cones produces layouts consistent with clinical acquisition.
    Section 3.3: an expert manually rotates and places segmentations; if the alignment is wrong, synthetic ultrasound realism is compromised.
  • standard math Variational autoencoder reparameterization and ELBO decomposition are valid background tools.
    Used in Section 3.2.1; standard background result from the VAE literature.

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Cite this review

Pith. "Pith review of DeepSPV: A Deep Learning Pipeline for 3D Spleen Volume Estimation from 2D Ultrasound Images." pith.science (2026). https://pith.science/paper/EE4FC7ZB

@misc{pith2026241111190,
  author       = {Pith},
  title        = {Pith review of: DeepSPV: A Deep Learning Pipeline for 3D Spleen Volume Estimation from 2D Ultrasound Images},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EE4FC7ZB}},
  note         = {Machine review of arXiv:2411.11190}
}
read the original abstract

Splenomegaly, the enlargement of the spleen, is an important clinical indicator for various associated medical conditions, such as sickle cell disease (SCD). Spleen length measured from 2D ultrasound is the most widely used metric for characterising spleen size. However, it is still considered a surrogate measure, and spleen volume remains the gold standard for assessing spleen size. Accurate spleen volume measurement typically requires 3D imaging modalities, such as computed tomography or magnetic resonance imaging, but these are not widely available, especially in the Global South which has a high prevalence of SCD. In this work, we introduce a deep learning pipeline, DeepSPV, for precise spleen volume estimation from single or dual 2D ultrasound images. The pipeline involves a segmentation network and a variational autoencoder for learning low-dimensional representations from the estimated segmentations. We investigate three approaches for spleen volume estimation and our best model achieves 86.62%/92.5% mean relative volume accuracy (MRVA) under single-view/dual-view settings, surpassing the performance of human experts. In addition, the pipeline can provide confidence intervals for the volume estimates as well as offering benefits in terms of interpretability, which further support clinicians in decision-making when identifying splenomegaly. We evaluate the full pipeline using a highly realistic synthetic dataset generated by a diffusion model, achieving an overall MRVA of 83.0% from a single 2D ultrasound image. Our proposed DeepSPV is the first work to use deep learning to estimate 3D spleen volume from 2D ultrasound images and can be seamlessly integrated into the current clinical workflow for spleen assessment.

Figures

Figures reproduced from arXiv: 2411.11190 by the authors.

Figure 1
Figure 1. An overview of this study. It first involves generating synthetic US images with the USDM, which is necessary due to the lack of paired 2D US images [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. An illustration of the proposed VAE-based framework. The latent space distribution is parameterised by the mean [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Illustration of three proposed VAE-based methods: (a) Nearest neighbour searching in the latent space (NN). (b) Post linear regression of latent [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: US images and their corresponding US layouts. The top row displays [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Illustration of the synthetic US layout creation process. The diagram [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: (a) US Semantic Diffusion Model (USDM). The framework transforms the noise from a standard Gaussian distribution to a realistic image through an iterative denoising process guided by the US layout x. (b) Semantic Diffusion Encoder Resblock (SDER) and Semantic Diffusion…
Figure 7
Figure 7. Figure 7: Two example CT volumes (top and bottom rows) with associated [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Illustration of manual measurements from CT images for spleen [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Robustness of single-view volume estimation to coronal in-plane ro [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 12
Figure 12. Figure 12: Illustration of the test latent spaces of RVAE trained with single [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]
Figure 13
Figure 13. Figure 13: Examples of real US images, synthetic US images generated from [PITH_FULL_IMAGE:figures/full_fig_p014_13.png]

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    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.