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DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures
Mathias Unberath, Jan-Nico Zaech, Sing Chun Lee, Bastian Bier, Javad Fotouhi, Mehran Armand, Nassir Navab
TL;DR
Fluoroscopy-guided procedures lack archived training images and reliable annotations, motivating synthetic data from diagnostic CT. DeepDRR combines learned material decomposition and scatter estimation with analytic projection and noise modeling, and models trained on it generalize to unseen clinical X-rays without retraining or domain adaptation. The framework therefore supports machine learning for fluoroscopy-guided procedures within the demonstrated pelvis landmark-detection task.
Problem
Fluoroscopy-guided procedures lack archived procedural images and well-established annotations for machine learning.
Method
DeepDRR simulates X-ray images from CT using learned material decomposition and scatter estimation combined with analytic forward projection and noise injection.
Results
Models trained on DeepDRRs generalized to unseen clinical X-ray data without retraining or domain adaptation, whereas models trained on conventional DRRs were unable to perform.
Takeaways & Limitations
DeepDRR supports establishing machine learning approaches for fluoroscopy-guided procedures by providing realistic synthetic training images from CT.
Takeaways & Limitations
Monte Carlo simulation is prohibitively expensive for generating training-set-sized DRRs on conventional hardware.
Abstract
from arXiv · showhide
Machine learning-based approaches outperform competing methods in most disciplines relevant to diagnostic radiology. Interventional radiology, however, has not yet benefited substantially from the advent of deep learning, in particular because of two reasons: 1) Most images acquired during the procedure are never archived and are thus not available for learning, and 2) even if they were available, annotations would be a severe challenge due to the vast amounts of data. When considering fluoroscopy-guided procedures, an interesting alternative to true interventional fluoroscopy is in silico simulation of the procedure from 3D diagnostic CT. In this case, labeling is comparably easy and potentially readily available, yet, the appropriateness of resulting synthetic data is dependent on the forward model. In this work, we propose DeepDRR, a framework for fast and realistic simulation of fluoroscopy and digital radiography from CT scans, tightly integrated with the software platforms native to deep learning. We use machine learning for material decomposition and scatter estimation in 3D and 2D, respectively, combined with analytic forward projection and noise injection to achieve the required performance. On the example of anatomical landmark detection in X-ray images of the pelvis, we demonstrate that machine learning models trained on DeepDRRs generalize to unseen clinically acquired data without the need for re-training or domain adaptation. Our results are promising and promote the establishment of machine learning in fluoroscopy-guided procedures.
1 Introduction
Deep learning has advanced diagnostic imaging, but fluoroscopy-guided intervention lacks suitable training data, annotations, and stable image conditions.
- Diagnostic imaging benefits from archived images, available or approximable annotations, and simple augmentation strategies, unlike interventional imaging.
- Only few procedural radiographs documenting outcomes are archived, creating a severe shortage of meaningful learning data.
- Interventional fluoroscopy also lacks well-established learning targets and exhibits substantial variability from factors such as surgical tools.
2 Methods
DeepDRR combines learned material decomposition and scatter estimation with analytic projection and realistic noise injection to simulate X-ray images from CT.
- DeepDRR combines a deep segmentation ConvNet, material- and spectrum-aware ray tracing, neural scatter estimation, and quantum and electronic noise injection.
- Material decomposition separates air, soft tissue, and bone in CT volumes, addressing cases where similar HU values obscure different attenuation characteristics.
- Analytic projection computes material contributions using imaging geometry, X-ray spectra, attenuation coefficients, and CT-derived material densities.
- A ten-layer ConvNet estimates Rayleigh scatter from downsampled projection-domain inputs, using large kernels in later layers to encourage smoothness.
- Fig. 2 compares input volumes, manual segmentations, and ConvNet outputs using volume renderings and coronal slices.
- Noise injection models photon statistics, pixel crosstalk, beam-hardening-dependent mean energy, and correlated electronic readout noise.
3 Experiments and Results
DeepDRR’s learned components achieved low segmentation and scatter-estimation errors while generating projection images in seconds. In a pelvic landmark-detection task, models trained on DeepDRRs were evaluated for generalization to unseen clinical X-ray images.
- 3.1 Framework Validation: 2.0 s per 615 × 479 projection, with runtime independent of photon count, supports rapid synthetic-image generation.The scatter estimator achieved a mean squared error of 6.4% of total scatter image energy.
- 3.1 Framework Validation: Volumetric segmentation of air, soft tissue, and bone produced a misclassification rate of (2.03 ± 3.63)%.This result was reported on CT volumes and was stated to be in line with prior results using the same architecture.
- 3.2 Task-based Evaluation: The pelvic landmark experiment tested whether models trained on synthetic DRRs could predict 23 landmarks in unseen clinical X-ray images.Models were trained using either conventional DRRs or DeepDRRs and evaluated on cadaver-study images acquired with a flat-panel C-arm system.
4 Discussion and Conclusion
The authors present DeepDRR as a fast, realistic CT-to-X-ray framework combining learned material decomposition and scatter estimation with analytic projection and noise injection. On pelvic landmark detection, DeepDRR-trained models generalized to clinical data without retraining or domain adaptation, whereas conventional-DRR training did not.
- 4 Discussion and Conclusion: DeepDRR combines learned 3D material decomposition and 2D scatter estimation with analytic energy- and material-dependent projection and noise injection.The framework is designed to generate realistic synthetic X-ray images from diagnostic 3D CT.
- 4 Discussion and Conclusion: Models trained on DeepDRRs generalized to clinical pelvic X-ray data without retraining or domain adaptation, while conventionally trained models were unable to perform.This conclusion was demonstrated on anatomical landmark prediction as a surrogate task.
- 4 Discussion and Conclusion: Future work will improve volumetric segmentation by adding materials such as metal and will expand scatter-estimation training data.These are the authors’ stated directions for improving the framework.