arxiv: 2604.12969 · v1 · submitted 2026-04-14 · 💻 cs.CV

Recognition: unknown

AbdomenGen: Sequential Volume-Conditioned Diffusion Framework for Abdominal Anatomy Generation

Yubraj Bhandari , Lavsen Dahal , Paul Segars , Joseph Y. Lo

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:16 UTC · model grok-4.3

classification 💻 cs.CV

keywords abdominal anatomy generationdiffusion modelsvolume control scalarmedical phantomsorgan mask synthesissequential conditioninganatomical coherencecontrollable generation

0 comments

The pith

A sequential diffusion model generates realistic abdominal anatomies while allowing independent volume control for each of 11 organs.

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

The paper presents AbdomenGen, a diffusion framework that builds abdominal organ masks one at a time. It introduces the Volume Control Scalar as a residual that separates organ size from overall body shape, so each organ can be enlarged or reduced without affecting the rest. Sequential conditioning on the body mask and earlier organs is used to keep the full anatomy coherent. The approach reaches solid geometric accuracy and supports targeted changes, such as enlarging the liver to match specific clinical cohorts.

Core claim

AbdomenGen synthesizes organ masks sequentially, conditioning each on the body mask and previously generated structures while incorporating the Volume Control Scalar (VCS) as a standardized residual; this decouples organ volume from body habitus and produces stable, disentangled modulation across 11 abdominal organs with measured geometric fidelity such as liver Dice 0.83 ± 0.05 and a 73.6% reduction in distributional distance for a hepatomegaly cohort via Wasserstein-based VCS selection.

What carries the argument

The Volume Control Scalar (VCS), a standardized residual that decouples organ size from body habitus and is injected during sequential diffusion steps to enable independent volume modulation while preserving global coherence.

If this is right

Stable single-organ calibration holds across the VCS interval from -3 to +3.
Multi-organ modulation stays disentangled so changes to one organ do not force changes in others.
Wasserstein-based VCS selection reduces the gap between generated and real data distributions by 73.6 percent in a hepatomegaly cohort.
The resulting phantoms support controlled simulation studies that vary organ sizes independently.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The framework could be used to create large synthetic datasets with precise anatomical variations for training segmentation or detection models.
It may allow researchers to simulate specific disease states by selectively scaling volumes of affected organs while holding others fixed.
Integration with existing phantom pipelines could reduce reliance on limited real-patient scans for imaging research.

Load-bearing premise

Sequential conditioning on the body mask, prior organs, and the VCS residual will maintain anatomical coherence and permit independent volume changes without creating artifacts or unrealistic organ relationships.

What would settle it

Visual or quantitative inspection of generated volumes at VCS values outside the training range to check whether spatial relationships between organs remain anatomically plausible and free of distortions when compared to real CT scans.

Figures

Figures reproduced from arXiv: 2604.12969 by Joseph Y. Lo, Lavsen Dahal, Paul Segars, Yubraj Bhandari.

**Figure 2.** Figure 2: Qualitative evaluation: Rows correspond to Liver (cyan, anteroposterior view) and Spleen (pink, posteroanterior view). Columns (left to right) show Reference Masks and VCS = −2, 0, and +2 respectively. As VCS varies, target organ volume (below each rendering) exhibits controlled changes while surrounding anatomical structures remain spatially coherent. (a) Cross-organ VCS calibration. (b) Distribution-le… view at source ↗

**Figure 3.** Figure 3: (a) Mean organ volume change (∆%) relative to VCS = 0 across 200 test cases (mean ± 95% CI) as VCS is swept over [-3, 3]. Volumes are normalized to baseline generation to enable cross-organ comparison. (b)Kernel density estimates of liver volumes for the training cohort (dotted), Merlin hepatomegaly Reference Masks (solid), and generated samples under the selected volumeconditioning setting (dashed). Ver… view at source ↗

**Figure 4.** Figure 4: Joint liver–spleen volume distributions under independent VCS condi [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

read the original abstract

Computational phantoms are widely used in medical imaging research, yet current systems to generate controlled, clinically meaningful anatomical variations remain limited. We present AbdomenGen, a sequential volume-conditioned diffusion framework for controllable abdominal anatomy generation. We introduce the \textbf{Volume Control Scalar (VCS)}, a standardized residual that decouples organ size from body habitus, enabling interpretable volume modulation. Organ masks are synthesized sequentially, conditioning on the body mask and previously generated structures to preserve global anatomical coherence while supporting independent, multi-organ control. Across 11 abdominal organs, the proposed framework achieves strong geometric fidelity (e.g., liver dice $0.83 \pm 0.05$), stable single-organ calibration over $[-3,+3]$ VCS, and disentangled multi-organ modulation. To showcase clinical utility with a hepatomegaly cohort selected from MERLIN, Wasserstein-based VCS selection reduces distributional distance of training data by 73.6\% . These results demonstrate calibrated, distribution-aware anatomical generation suitable for controllable abdominal phantom construction and simulation studies.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

AbdomenGen adds a volume control scalar and sequential conditioning to diffusion models for abdominal phantoms, but the abstract leaves the validation and coherence checks too thin to judge the claims yet.

read the letter

The main thing to know is that this paper introduces a Volume Control Scalar as a standardized residual to separate organ size from overall body shape, then generates the 11 abdominal organs one by one in a diffusion process conditioned on the body mask and earlier organs. That combination is new relative to the unconditional or single-organ diffusion work they cite, and it targets a real need for controllable computational phantoms in medical imaging simulation. They report a liver Dice of 0.83 plus or minus 0.05, stable single-organ behavior across a VCS range of -3 to +3, and a 73.6 percent reduction in distributional distance for a hepatomegaly example using Wasserstein selection. Those numbers suggest the approach can produce usable geometry and some level of independent control. The practical angle for phantom construction and reducing dependence on real patient data is clear enough that people in that niche would find it worth a look. The soft spots are the lack of any baselines, ablations, or error analysis in what we have, plus no reported checks on inter-organ correlations, overlap rates, or order sensitivity when multiple VCS values are changed together. The sequential setup makes error propagation a live possibility, and without those quantitative safeguards the disentanglement claim stays provisional. The Wasserstein reduction is interesting but needs the exact comparison set spelled out to mean much. This is for researchers building synthetic abdominal datasets or running imaging studies that need tunable anatomy. It shows clear thinking on the control problem even if the evidence is still light. I would send it to peer review so the methods and full results can be examined properly.

Referee Report

3 major / 1 minor

Summary. The paper introduces AbdomenGen, a sequential volume-conditioned diffusion model for generating controllable abdominal anatomical phantoms. It proposes the Volume Control Scalar (VCS) as a standardized residual to decouple organ volumes from body habitus, enabling independent modulation of 11 organs while using sequential conditioning on the body mask and prior organs to maintain coherence. Empirical results include liver Dice of 0.83 ± 0.05, stable single-organ calibration over VCS ∈ [-3, +3], disentangled multi-organ control, and a 73.6% reduction in distributional distance for a hepatomegaly cohort via Wasserstein-based VCS selection from the MERLIN dataset.

Significance. If validated, the work could provide a practical tool for creating distribution-aware, controllable computational phantoms useful in medical imaging simulation and research on anatomical variations. The VCS mechanism offers an interpretable, parameter-light way to modulate volumes independently, and the sequential diffusion approach targets global coherence in a way that could generalize beyond the abdomen if the empirical claims are substantiated with fuller controls.

major comments (3)

[Abstract] Abstract: The reported geometric fidelity (liver Dice 0.83 ± 0.05 across 11 organs) is presented without any baseline comparisons to prior abdominal organ generation or segmentation methods, nor ablations isolating the contribution of sequential conditioning versus the VCS residual; this makes it impossible to determine whether the numbers reflect a genuine advance or data-specific effects.
[Abstract] Abstract: The central claim that sequential conditioning on body mask + prior organs + VCS residual produces independent per-organ volume scaling while preserving global coherence lacks quantitative support such as inter-organ volume correlation matrices, conditional overlap rates, or order-ablation results when multiple VCS values are varied simultaneously (especially in the hepatomegaly use-case).
[Abstract] Abstract: The 73.6% reduction in distributional distance via Wasserstein-based VCS selection for the hepatomegaly cohort is stated without specifying the exact selection protocol, whether it is post-hoc on the full dataset, or any held-out validation, which bears directly on the claim of distribution-aware generation.

minor comments (1)

[Abstract] Abstract: The standardization process and exact definition of the VCS residual (how it is computed from organ volume and body habitus) are not detailed, leaving the 'parameter-free' decoupling claim difficult to evaluate from the given text.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important aspects of clarity and evidence in the abstract, and we have revised the manuscript to address them directly while preserving the original contributions.

read point-by-point responses

Referee: [Abstract] Abstract: The reported geometric fidelity (liver Dice 0.83 ± 0.05 across 11 organs) is presented without any baseline comparisons to prior abdominal organ generation or segmentation methods, nor ablations isolating the contribution of sequential conditioning versus the VCS residual; this makes it impossible to determine whether the numbers reflect a genuine advance or data-specific effects.

Authors: We agree that the abstract's brevity omitted explicit baselines and ablations, which limits immediate assessment of the advance. The full manuscript contextualizes results against related diffusion-based generation approaches in the introduction and discussion. To address this, we have revised the abstract to briefly reference prior methods and note the targeted improvements in coherence and controllability. We have also added an ablation study in the revised results section isolating sequential conditioning from the VCS residual, enabling clearer evaluation of each component's contribution. revision: yes
Referee: [Abstract] Abstract: The central claim that sequential conditioning on body mask + prior organs + VCS residual produces independent per-organ volume scaling while preserving global coherence lacks quantitative support such as inter-organ volume correlation matrices, conditional overlap rates, or order-ablation results when multiple VCS values are varied simultaneously (especially in the hepatomegaly use-case).

Authors: The original results include single-organ calibration curves and multi-organ generation examples to illustrate the claim, but we concur that additional quantitative metrics would provide stronger support. In the revision, we have added inter-organ volume correlation matrices demonstrating low correlations (supporting independence), conditional overlap rates to quantify coherence, and order-ablation experiments for simultaneous multi-VCS variations in the hepatomegaly scenario. These new analyses are now referenced in the updated abstract and detailed in the results. revision: yes
Referee: [Abstract] Abstract: The 73.6% reduction in distributional distance via Wasserstein-based VCS selection for the hepatomegaly cohort is stated without specifying the exact selection protocol, whether it is post-hoc on the full dataset, or any held-out validation, which bears directly on the claim of distribution-aware generation.

Authors: The abstract summarizes the outcome, with the full manuscript describing the Wasserstein distance computation in the experiments section. We have revised the abstract and methods to explicitly state that VCS selection matches the target distribution using the training data as reference for this demonstration (hence post-hoc on the cohort). The protocol details are now expanded, including the exact Wasserstein formulation. We acknowledge the benefit of held-out validation and have added a note discussing its implications, though the original result remains an illustrative use-case on the provided data. revision: partial

Circularity Check

0 steps flagged

No significant circularity; claims rest on empirical validation

full rationale

The paper introduces a sequential volume-conditioned diffusion model and the VCS residual as a modeling choice, then reports empirical outcomes (Dice scores, calibration ranges, Wasserstein distance reduction). No derivation chain, equation, or 'prediction' reduces by construction to a fitted parameter, self-citation, or ansatz smuggled from prior work. The Wasserstein selection is an explicit optimization step whose reported reduction follows directly from the selection criterion rather than from any model-derived result. The central claims therefore remain self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The framework relies on standard diffusion model training assumptions and the unproven claim that VCS acts as a true residual decoupling volume from habitus; no explicit free parameters are named in the abstract, but the diffusion process itself contains many implicit hyperparameters.

axioms (2)

domain assumption Diffusion models can be conditioned on masks and prior generations to produce anatomically coherent outputs
Invoked throughout the sequential generation description in the abstract.
ad hoc to paper VCS functions as a standardized residual that decouples organ size from body habitus
Central to the controllability claim; introduced without derivation in the abstract.

invented entities (1)

Volume Control Scalar (VCS) no independent evidence
purpose: Standardized residual for interpretable, independent organ volume modulation
New scalar introduced to enable the claimed disentangled control; no independent evidence outside the generated outputs is provided in the abstract.

pith-pipeline@v0.9.0 · 5487 in / 1461 out tokens · 29719 ms · 2026-05-10T16:16:25.884830+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 8 canonical work pages · 2 internal anchors

[1]

Research Square pp

Blankemeier, L., Cohen, J.P., Kumar, A., Van Veen, D., Gardezi, S.J.S., Paschali, M., Chen, Z., Delbrouck, J.B., Reis, E., Truyts, C., et al.: Merlin: A vision language foundation model for 3d computed tomography. Research Square pp. rs–3 (2024)

2024
[2]

MONAI: An open-source framework for deep learning in healthcare

Cardoso, M.J., Li, W., Brown, R., Ma, N., Kerfoot, E., Wang, Y., Murrey, B., Myronenko, A., Zhao, C., Yang, D., et al.: Monai: An open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701 (2022)

work page internal anchor Pith review arXiv 2022
[3]

Computers & Graphics99, 234–246 (2021)

Chheang, V., Saalfeld, P., Joeres, F., Boedecker, C., Huber, T., Huettl, F., Lang, H., Preim, B., Hansen, C.: A collaborative virtual reality environment for liver surgery planning. Computers & Graphics99, 234–246 (2021)

2021
[4]

In: Proceedings of the IEEE/CVF international confer- ence on computer vision

Chou, G., Bahat, Y., Heide, F.: Diffusion-sdf: Conditional generative modeling of signed distance functions. In: Proceedings of the IEEE/CVF international confer- ence on computer vision. pp. 2262–2272 (2023)

2023
[5]

Medical Image Analysis p

Dahal, L., Ghojoghnejad, M., Vancoillie, L., Ghosh, D., Bhandari, Y., Kim, D., Ho, F.C., Tushar, F.I., Luo, S., Lafata, K.J., et al.: Xcat 3.0: A comprehensive library of personalized digital twins derived from ct scans. Medical Image Analysis p. 103636 (2025)

2025
[6]

Guo et al

Guo, P., Zhao, C., Yang, D., Xu, Z., Nath, V., Tang, Y., Simon, B., Belue, M., Harmon, S., Turkbey, B., Xu, D.: Maisi: Medical ai for synthetic imaging. arXiv preprint arXiv:2409.11169 (2024), https://arxiv.org/abs/2409.11169, preprint

work page arXiv 2024
[7]

Advances in neural information processing systems33, 6840–6851 (2020)

Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems33, 6840–6851 (2020)

2020
[8]

In: Shape in Medical Imaging, Lecture Notes in Computer Science, vol

Li, J., Pepe, A., Luijten, G., Schwarz-Gsaxner, C., Kleesiek, J., Egger, J.: Anatomy completor: A multi-class completion framework for 3d anatomy reconstruction. In: Shape in Medical Imaging, Lecture Notes in Computer Science, vol. 14350, pp. 1–14. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-46914-5_1

work page doi:10.1007/978-3-031-46914-5_1 2023
[9]

Toward Realistic AI-Generated Student Questions to Support Instructor Training

Mouheb, K., Nejad, M.G., Dahal, L., Samei, E., Lafata, K.J., Segars, W.P., Lo, J.Y.: Large intestine 3d shape refinement using conditional latent point diffusion models. In: Shape in Medical Imaging, Lecture Notes in Computer Science, vol. 16171, pp. 103–116. Springer, Cham (2025). https://doi.org/10.1007/978-3-032- 06774-6_8

work page doi:10.1007/978-3-032- 2025
[10]

Surgery166(3), 247–253 (2019)

Olthof, P.B., van Dam, R., Jovine, E., Campos, R.R., de Santibañes, E., Oldhafer, K., Malago, M., Abdalla, E.K., Schadde, E.: Accuracy of estimated total liver volume formulas before liver resection. Surgery166(3), 247–253 (2019)

2019
[11]

In: Proceedings of the AAAI conference on artificial intelligence

Perez, E., Strub, F., De Vries, H., Dumoulin, V., Courville, A.: Film: Visual rea- soning with a general conditioning layer. In: Proceedings of the AAAI conference on artificial intelligence. vol. 32 (2018)

2018
[12]

In: Shape in Medical Imaging, Lecture Notes in Computer Science, vol

Podobnik, G., Balodi, N., Killeen, B.D., Vrtovec, T., Unberath, M.: Anatomygen: Generating anatomically plausible human phantoms at high resolution. In: Shape in Medical Imaging, Lecture Notes in Computer Science, vol. 16171, pp. 129–139. Springer, Cham (2025). https://doi.org/10.1007/978-3-032-06774-6_10

work page doi:10.1007/978-3-032-06774-6_10 2025
[13]

European radiology7(2), 246–248 (1997)

Prassopoulos, P., Daskalogiannaki, M., Raissaki, M., Hatjidakis, A., Gourtsoyian- nis, N.: Determination of normal splenic volume on computed tomography in re- lation to age, gender and body habitus. European radiology7(2), 246–248 (1997)

1997
[14]

Medical Physics37(9), 4902–4915 (2010)

Segars, W.P., Sturgeon, G., Mendonca, S., Grimes, J., Tsui, B.M.W.: 4d xcat phan- tom for multimodality imaging research. Medical Physics37(9), 4902–4915 (2010). https://doi.org/10.1118/1.3480985

work page doi:10.1118/1.3480985 2010
[15]

Denoising Diffusion Implicit Models

Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020) 10 Anonymized Author et al

work page internal anchor Pith review Pith/arXiv arXiv 2010
[16]

In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Takikawa, T., Litalien, J., Yin, K., Kreis, K., Loop, C., Nowrouzezahrai, D., Ja- cobson, A., McGuire, M., Fidler, S.: Neural geometric level of detail: Real-time rendering with implicit 3d shapes. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11358–11367 (2021)

2021
[17]

Scientific reports11(1), 9068 (2021)

Tustison, N.J., Cook, P.A., Holbrook, A.J., Johnson, H.J., Muschelli, J., Devenyi, G.A., Duda, J.T., Das, S.R., Cullen, N.C., Gillen, D.L., et al.: The antsx ecosystem for quantitative biological and medical imaging. Scientific reports11(1), 9068 (2021)

2021
[18]

Parameterization of multiple Bragg curves for scanning proton beams using simultaneous fitting of multiple curves

Xu, X.G.: An exponential growth of computational phantom research in radiation protection, imaging, and radiotherapy: A review of the fifty-year history. Physics in Medicine & Biology59(18), R233–R302 (2014). https://doi.org/10.1088/0031- 9155/59/18/R233

work page doi:10.1088/0031- 2014