DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures

Abstract: 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.

• The paper presents the DeepDRR framework that synthesizes realistic fluoroscopy images from CT data for machine learning applications.
• It employs convolutional neural networks for material segmentation and scatter estimation, surpassing traditional ray-tracing methods.
• Experiments on anatomical landmark detection show that models trained on synthetic data from DeepDRR effectively generalize to clinical scenarios.

Overview of "DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures"

The paper presents DeepDRR, an innovative framework designed to facilitate the implementation of machine learning in fluoroscopy-guided interventions. Fluoroscopy-guided procedures, unlike diagnostic radiology, have largely lagged in leveraging machine learning due to the lack of archived imaging data and the difficulty of labeling the vast amounts of data generated during interventions. This research puts forward a simulation-based solution, generating synthetic fluoroscopy images from 3D CT data, to address these challenges.

Core Contributions

1. DeepDRR Framework: The framework introduces a novel approach that simulates realistic Digital Radiography (DR) and fluoroscopic images from CT scans. By integrating machine learning methods for material decomposition and scatter estimation with analytical forward projection and noise modeling, DeepDRR stands out as an efficient tool for generating synthetic radiographs suitable for training purposes.
2. Material Decomposition and Scatter Estimation: The study employs convolutional neural networks (ConvNets) for segmenting materials within CT volumes and predicting scatter distribution in projection images. This method overcomes the limitations of traditional ray-tracing by integrating material-specific attenuation data and statistical photon transport models, thus offering enhanced realism in the simulation.
3. Noise Modeling: Quantum noise and electronic readout noise are realistically introduced into simulations, mimicking the physical characteristics of fluoroscopic imaging systems. This adds a layer of authenticity to the synthetically generated images, improving the quality of training data.

Experimental Findings

The researchers evaluate the DeepDRR framework using the task of anatomical landmark detection in pelvic X-ray images. Trained models on DeepDRRs were able to generalize directly to unseen clinical fluoroscopy data without the need for domain adaptation, thereby demonstrating the applicability and effectiveness of DeepDRR in real-world clinical data training scenarios. The numerical performance, specifically on landmark detection, shows promising transfer learning capabilities when simulations match clinical variances closely.

Implications and Future Work

The implications of DeepDRR are significant for both theoretical and practical fronts in medical imaging AI. Practically, it offers an immediate solution to the lack of annotated data in interventional radiology by creating a bridge using synthetically generated data. Theoretically, this paves the way for assessing the role of synthetic data across various machine learning paradigms, particularly focusing on its efficacy in enhancing the adaptability and robustness of models.

Future directions include refining the volumetric segmentation to encompass a broader range of materials, such as metal, which is prevalent in interventional settings. Additionally, enhancing the scatter estimation may further improve the simulation's realism, thereby broadening the applicability of DeepDRR in different procedural contexts.

In conclusion, DeepDRR is positioned as a catalyst for the advancement of machine learning applications in fluoroscopy-guided interventions, promising to fill the current gap of scarce annotated data and fostering the broader adoption of AI in interventional radiology.