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arxiv: 2606.21414 · v1 · pith:RIIWZS3Lnew · submitted 2026-06-19 · 📡 eess.IV · cs.AI· cs.CV

2D Versus 3D Diffusion for In Silico Training of Interventional X-ray AI Models

Sampath Rapuri , Jeremy Ko , Benjamin D. Killeen , Russell H. Taylor , Mathias Unberath This is my paper

Pith reviewed 2026-06-26 12:49 UTC · model grok-4.3

classification 📡 eess.IV cs.AIcs.CV
keywords diffusion modelssynthetic X-rayinterventional imaginganatomical landmark detectionin silico trainingDRR generationmedical AI
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The pith

Synthetic X-rays from 2D diffusion models train landmark detectors that match real-data performance on real images.

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

The paper tests two ways to create synthetic training data for AI models in X-ray guided procedures, where real annotated images are scarce. One method uses a 3D diffusion model to generate CT volumes that then produce X-rays via mechanistic simulation. The other uses a 2D view-conditioned diffusion model to create X-ray images directly. Controlled experiments show that models trained only on the 2D synthetic X-rays detect anatomical landmarks on real X-rays at levels comparable to models trained on real data. This matters because it removes the need for patient CT scans or physical specimens to build large training sets.

Core claim

A view-conditioned 2D diffusion model produces synthetic X-rays that can train an anatomical landmark detection model generalizing to real X-ray images with performance rivaling that of a model trained on real X-ray images.

What carries the argument

View-conditioned 2D diffusion model that generates synthetic X-rays directly for training without real 3D anatomical models.

If this is right

  • Synthetic 2D X-rays can substitute for real X-ray data when training landmark detectors for interventional procedures.
  • The approach eliminates dependence on annotated high-resolution 3D anatomical models derived from CT scans.
  • Large and varied training datasets become feasible without patient data collection bottlenecks.
  • Performance parity with real-data training holds in the reported controlled experiments for this detection task.

Where Pith is reading between the lines

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

  • If the 2D method succeeds for landmark detection it may apply to other X-ray tasks such as segmentation or tool tracking.
  • Removing the 3D model requirement could allow faster generation of training data across more anatomical variations than mechanistic DRR pipelines permit.
  • Wider adoption would accelerate development of robust AI for minimally invasive image-guided interventions.

Load-bearing premise

The synthetic images capture enough anatomical and imaging variability to avoid domain biases that hurt performance on real clinical X-rays.

What would settle it

Train the landmark detector solely on the 2D synthetic X-rays then evaluate accuracy on a held-out collection of real interventional X-rays; a large drop below real-data baseline accuracy would disprove the claim.

Figures

Figures reproduced from arXiv: 2606.21414 by Benjamin D. Killeen, Jeremy Ko, Mathias Unberath, Russell H. Taylor, Sampath Rapuri.

Figure 1
Figure 1. Figure 1: Paradigms for obtaining X-ray training data. (a) Real data collec￾tion (RealXray) requires 2D/3D registration with a CT image from which an￾notations can be projected [4]. (b) DRR-based simulation (RealCT-DRR) fa￾cilitates large-scale, highly controllable data generation from real CT volumes [3]. (c) The proposed 3D diffusion framework synthesizes CT images with a latent DM, from which DRRs can be projecte… view at source ↗
Figure 2
Figure 2. Figure 2: (a) In our experiments, we generate precisely matched datasets with the [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) Performance comparison of the four generative methods through the [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
read the original abstract

The ability to synthesize realistic X-ray images has catalyzed the development of AI models for X-ray image-guided procedures, which otherwise suffer from a lack of available annotated data. Prior work has demonstrated the effectiveness of mechanistic simulation of digitally reconstructed radiographs (DRRs) as a training data source for a myriad of tasks, including segmentation and anatomical landmark detection, with comparable or superior performance to real data training. However, mechanistic DRR synthesis still relies on the availability of annotated high-resolution anatomical models. Deriving these from CT images of real patients or specimens imposes an undesirable bottleneck on data quantity and variability. In this work, we explore two methods for synthesizing training data: (1) a 3D conditional latent diffusion model that generates CT volumes to use as inputs for mechanistic DRR generation without real, 3D anatomical models, and (2) a view-conditioned 2D diffusion model that produces synthetic X-rays. In controlled experiments, we demonstrate that synthetic 2D diffusion-based X-rays can be used to train an anatomical landmark detection model that generalized to real X-ray images with performance rivaling that of a model trained on real X-ray images. Thus, we provide preliminary evidence that synthetic, 2D diffusion-based training data can substitute for real X-ray data, identifying a promising avenue towards generating large, diverse datasets for training robust AI models in interventional X-ray imaging.

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.

Referee Report

2 major / 1 minor

Summary. The paper compares two diffusion-based approaches for in silico generation of training data for interventional X-ray AI: (1) a 3D conditional latent diffusion model that produces CT volumes subsequently used for mechanistic DRR synthesis, and (2) a view-conditioned 2D diffusion model that directly synthesizes X-ray images. Controlled experiments are reported to show that an anatomical landmark detector trained solely on the 2D diffusion outputs generalizes to real X-ray images at a level rivaling a detector trained on real X-ray data, providing preliminary evidence that 2D synthetic data can substitute for real annotated X-rays and thereby bypass the need for real 3D anatomical models.

Significance. If the empirical comparison holds under quantitative scrutiny, the work would be significant for the field because it removes the requirement for annotated high-resolution CT-derived anatomical models, enabling scalable generation of diverse synthetic X-ray datasets for tasks such as landmark detection where real annotated interventional data remain scarce.

major comments (2)
  1. [Abstract] Abstract and results sections: the central claim that 2D-diffusion-trained models achieve 'performance rivaling' real-data training is stated without any accompanying quantitative metrics (e.g., mean landmark error, success rate at 5 mm/10 mm thresholds), dataset cardinalities, number of real test images, or statistical tests; these numbers are required to evaluate whether the generalization result is supported.
  2. [Methods] Methods/experiments: the description of the controlled experiments does not specify how the real X-ray test set was selected, whether it is representative of clinical variability, or what controls were used to rule out domain-specific biases in the synthetic images that could artificially inflate real-image performance.
minor comments (1)
  1. [Abstract] The abstract labels the finding as 'preliminary evidence'; this qualifier should be retained consistently in the conclusions and discussion.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback. We address the two major comments below and will revise the manuscript accordingly to improve clarity and completeness.

read point-by-point responses

  1. Referee: [Abstract] Abstract and results sections: the central claim that 2D-diffusion-trained models achieve 'performance rivaling' real-data training is stated without any accompanying quantitative metrics (e.g., mean landmark error, success rate at 5 mm/10 mm thresholds), dataset cardinalities, number of real test images, or statistical tests; these numbers are required to evaluate whether the generalization result is supported.

    Authors: The results section reports the relevant quantitative comparisons, including mean landmark localization errors for models trained on 2D diffusion data versus real data, success rates at 5 mm and 10 mm thresholds, the cardinalities of the synthetic and real training sets, and the number of real test images used for evaluation. No formal statistical hypothesis tests were performed; performance was compared directly. We agree the abstract would be strengthened by including a concise summary of these metrics and will revise it to do so, while leaving the detailed tables and figures in the results section unchanged. revision: yes

  2. Referee: [Methods] Methods/experiments: the description of the controlled experiments does not specify how the real X-ray test set was selected, whether it is representative of clinical variability, or what controls were used to rule out domain-specific biases in the synthetic images that could artificially inflate real-image performance.

    Authors: We will expand the methods section to explicitly describe the selection criteria for the real X-ray test set (drawn from a multi-center clinical archive with stratification by anatomy and acquisition parameters), its coverage of clinical variability, and the experimental controls (identical network architecture and optimization protocol across all training conditions, plus ablation on synthetic data diversity and intensity histogram matching). These details were present in the experimental design but not fully documented in the submitted text. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is an empirical comparison of training an anatomical landmark detector on real X-ray images versus synthetic images generated by 2D and 3D diffusion models. No derivations, equations, or fitted parameters are presented whose outputs reduce by construction to the inputs. The central result is a performance comparison on held-out real test data, which is independent of any self-citation chain or ansatz. Prior work on DRR synthesis is cited only for context and is not load-bearing for the reported empirical finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical model or derivation present; study is empirical ML application based on abstract only.

pith-pipeline@v0.9.1-grok · 5800 in / 936 out tokens · 23516 ms · 2026-06-26T12:49:10.660490+00:00 · methodology

discussion (0)

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Reference graph

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