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Surgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. While SPR based on video sources is well-established, incorporation of interventional X-ray sequences has not yet been explored. This paper presents Pelphix, a first approach to SPR for X-ray-guided percutaneous pelvic fracture fixation, which models the procedure at four levels of granularity - corridor, activity, view, and frame value - simulating the pelvic fracture fixation workflow as a Markov process to provide fully annotated training data. Using added supervision from detection of bony corridors, tools, and anatomy, we learn image representations that are fed into a transformer model to regress surgical phases at the four granularity levels. Our approach demonstrates the feasibility of X-ray-based SPR, achieving an average accuracy of 99.2% on simulated sequences and 71.7% in cadaver across all granularity levels, with up to 84% accuracy for the target corridor in real data. This work constitutes the first step toward SPR for the X-ray domain, establishing an approach to categorizing phases in X-ray-guided surgery, simulating realistic image sequences to enable machine learning model development, and demonstrating that this approach is feasible for the analysis of real procedures. As X-ray-based SPR continues to mature, it will benefit procedures in orthopedic surgery, angiography, and interventional radiology by equipping intelligent surgical systems with situational awareness in the operating room.

Purpose: The use of robotic continuum manipulators has been proposed to facilitate less-invasive orthopedic surgical procedures. While tools and strategies have been developed, critical challenges such as system control and intra-operative guidance are under-addressed. Simulation tools can help solve these challenges, but several gaps limit their utility for orthopedic surgical systems, particularly those with continuum manipulators. Herein, a simulation platform which addresses these gaps is presented as a tool to better understand and solve challenges for minimally invasive orthopedic procedures., Methods: An open-source surgical simulation software package was developed in which a continuum manipulator can interact with any volume model such as to drill bone volumes segmented from a 3D computed tomography (CT) image. Paired simulated X-ray images of the scene can also be generated. As compared to previous works, tool-anatomy interactions use a physics-based approach which leads to more stable behavior and wider procedure applicability. A new method for representing low-level volumetric drilling behavior is also introduced to capture material variability within bone as well as patient-specific properties from a CT., Results: Similar interaction between a continuum manipulator and phantom bone was also demonstrated between a simulated manipulator and volumetric obstacle models. High-level material- and tool-driven behavior was shown to emerge directly from the improved low-level interactions, rather than by need of manual programming., Conclusion: This platform is a promising tool for developing and investigating control algorithms for tasks such as curved drilling. The generation of simulated X-ray images that correspond to the scene is useful for developing and validating image guidance models. The improvements to volumetric drilling offer users the ability to better tune behavior for specific tools and procedures and enable research to improve surgical simulation model fidelity. This platform will be used to develop and test control algorithms for image-guided curved drilling procedures in the femur., (© 2023. CARS.)

Purpose: Percutaneous fracture fixation involves multiple X-ray acquisitions to determine adequate tool trajectories in bony anatomy. In order to reduce time spent adjusting the X-ray imager's gantry, avoid excess acquisitions, and anticipate inadequate trajectories before penetrating bone, we propose an autonomous system for intra-operative feedback that combines robotic X-ray imaging and machine learning for automated image acquisition and interpretation, respectively., Methods: Our approach reconstructs an appropriate trajectory in a two-image sequence, where the optimal second viewpoint is determined based on analysis of the first image. A deep neural network is responsible for detecting the tool and corridor, here a K-wire and the superior pubic ramus, respectively, in these radiographs. The reconstructed corridor and K-wire pose are compared to determine likelihood of cortical breach, and both are visualized for the clinician in a mixed reality environment that is spatially registered to the patient and delivered by an optical see-through head-mounted display., Results: We assess the upper bounds on system performance through in silico evaluation across 11 CTs with fractures present, in which the corridor and K-wire are adequately reconstructed. In post hoc analysis of radiographs across 3 cadaveric specimens, our system determines the appropriate trajectory to within 2.8 ± 1.3 mm and 2.7 ± 1.8[Formula: see text]., Conclusion: An expert user study with an anthropomorphic phantom demonstrates how our autonomous, integrated system requires fewer images and lower movement to guide and confirm adequate placement compared to current clinical practice. Code and data are available., (© 2023. CARS.)

Purpose: Image-guided navigation and surgical robotics are the next frontiers of minimally invasive surgery. Assuring safety in high-stakes clinical environments is critical for their deployment. 2D/3D registration is an essential, enabling algorithm for most of these systems, as it provides spatial alignment of preoperative data with intraoperative images. While these algorithms have been studied widely, there is a need for verification methods to enable human stakeholders to assess and either approve or reject registration results to ensure safe operation., Methods: To address the verification problem from the perspective of human perception, we develop novel visualization paradigms and use a sampling method based on approximate posterior distribution to simulate registration offsets. We then conduct a user study with 22 participants to investigate how different visualization paradigms (Neutral, Attention-Guiding, Correspondence-Suggesting) affect human performance in evaluating the simulated 2D/3D registration results using 12 pelvic fluoroscopy images., Results: All three visualization paradigms allow users to perform better than random guessing to differentiate between offsets of varying magnitude. The novel paradigms show better performance than the neutral paradigm when using an absolute threshold to differentiate acceptable and unacceptable registrations (highest accuracy: Correspondence-Suggesting (65.1%), highest F1 score: Attention-Guiding (65.7%)), as well as when using a paradigm-specific threshold for the same discrimination (highest accuracy: Attention-Guiding (70.4%), highest F1 score: Corresponding-Suggesting (65.0%))., Conclusion: This study demonstrates that visualization paradigms do affect the human-based assessment of 2D/3D registration errors. However, further exploration is needed to understand this effect better and develop more effective methods to assure accuracy. This research serves as a crucial step toward enhanced surgical autonomy and safety assurance in technology-assisted image-guided surgery., (© 2023. CARS.)

Differentiable rendering is a technique to connect 3D scenes with corresponding 2D images. Since it is differentiable, processes during image formation can be learned. Previous approaches to differentiable rendering focus on mesh-based representations of 3D scenes, which is inappropriate for medical applications where volumetric, voxelized models are used to represent anatomy. We propose a novel Projective Spatial Transformer module that generalizes spatial transformers to projective geometry, thus enabling differentiable volume rendering. We demonstrate the usefulness of this architecture on the example of 2D/3D registration between radiographs and CT scans. Specifically, we show that our transformer enables end-to-end learning of an image processing and projection model that approximates an image similarity function that is convex with respect to the pose parameters, and can thus be optimized effectively using conventional gradient descent. To the best of our knowledge, we are the first to describe the spatial transformers in the context of projective transmission imaging, including rendering and pose estimation. We hope that our developments will benefit related 3D research applications. The source code is available at https://github.com/gaocong13/Projective-Spatial-Transformers.

Purpose: Fluoroscopy is the standard imaging modality used to guide hip surgery and is therefore a natural sensor for computer-assisted navigation. In order to efficiently solve the complex registration problems presented during navigation, human-assisted annotations of the intraoperative image are typically required. This manual initialization interferes with the surgical workflow and diminishes any advantages gained from navigation. In this paper, we propose a method for fully automatic registration using anatomical annotations produced by a neural network., Methods: Neural networks are trained to simultaneously segment anatomy and identify landmarks in fluoroscopy. Training data are obtained using a computationally intensive, intraoperatively incompatible, 2D/3D registration of the pelvis and each femur. Ground truth 2D segmentation labels and anatomical landmark locations are established using projected 3D annotations. Intraoperative registration couples a traditional intensity-based strategy with annotations inferred by the network and requires no human assistance., Results: Ground truth segmentation labels and anatomical landmarks were obtained in 366 fluoroscopic images across 6 cadaveric specimens. In a leave-one-subject-out experiment, networks trained on these data obtained mean dice coefficients for left and right hemipelves, left and right femurs of 0.86, 0.87, 0.90, and 0.84, respectively. The mean 2D landmark localization error was 5.0 mm. The pelvis was registered within [Formula: see text] for 86% of the images when using the proposed intraoperative approach with an average runtime of 7 s. In comparison, an intensity-only approach without manual initialization registered the pelvis to [Formula: see text] in 18% of images., Conclusions: We have created the first accurately annotated, non-synthetic, dataset of hip fluoroscopy. By using these annotations as training data for neural networks, state-of-the-art performance in fluoroscopic segmentation and landmark localization was achieved. Integrating these annotations allows for a robust, fully automatic, and efficient intraoperative registration during fluoroscopic navigation of the hip.

Purpose: Minimally invasive alternatives are now available for many complex surgeries. These approaches are enabled by the increasing availability of intra-operative image guidance. Yet, fluoroscopic X-rays suffer from projective transformation and thus cannot provide direct views onto anatomy. Surgeons could highly benefit from additional information, such as the anatomical landmark locations in the projections, to support intra-operative decision making. However, detecting landmarks is challenging since the viewing direction changes substantially between views leading to varying appearance of the same landmark. Therefore, and to the best of our knowledge, view-independent anatomical landmark detection has not been investigated yet., Methods: In this work, we propose a novel approach to detect multiple anatomical landmarks in X-ray images from arbitrary viewing directions. To this end, a sequential prediction framework based on convolutional neural networks is employed to simultaneously regress all landmark locations. For training, synthetic X-rays are generated with a physically accurate forward model that allows direct application of the trained model to real X-ray images of the pelvis. View invariance is achieved via data augmentation by sampling viewing angles on a spherical segment of [Formula: see text]., Results: On synthetic data, a mean prediction error of 5.6 ± 4.5 mm is achieved. Further, we demonstrate that the trained model can be directly applied to real X-rays and show that these detections define correspondences to a respective CT volume, which allows for analytic estimation of the 11 degree of freedom projective mapping., Conclusion: We present the first tool to detect anatomical landmarks in X-ray images independent of their viewing direction. Access to this information during surgery may benefit decision making and constitutes a first step toward global initialization of 2D/3D registration without the need of calibration. As such, the proposed concept has a strong prospect to facilitate and enhance applications and methods in the realm of image-guided surgery.

Purpose: Machine learning-based approaches now outperform competing methods in most disciplines relevant to diagnostic radiology. Image-guided procedures, however, have not yet benefited substantially from the advent of deep learning, in particular because images for procedural guidance are not archived and thus unavailable for learning, and even if they were available, annotations would be a severe challenge due to the vast amounts of data. In silico simulation of X-ray images from 3D CT is an interesting alternative to using true clinical radiographs since labeling is comparably easy and potentially readily available., Methods: We extend our framework for fast and realistic simulation of fluoroscopy from high-resolution CT, called DeepDRR, with tool modeling capabilities. The framework is publicly available, open source, and tightly integrated with the software platforms native to deep learning, i.e., Python, PyTorch, and PyCuda. DeepDRR relies on machine learning for material decomposition and scatter estimation in 3D and 2D, respectively, but uses analytic forward projection and noise injection to ensure acceptable computation times. On two X-ray image analysis tasks, namely (1) anatomical landmark detection and (2) segmentation and localization of robot end-effectors, we demonstrate that convolutional neural networks (ConvNets) trained on DeepDRRs generalize well to real data without re-training or domain adaptation. To this end, we use the exact same training protocol to train ConvNets on naïve and DeepDRRs and compare their performance on data of cadaveric specimens acquired using a clinical C-arm X-ray system., Results: Our findings are consistent across both considered tasks. All ConvNets performed similarly well when evaluated on the respective synthetic testing set. However, when applied to real radiographs of cadaveric anatomy, ConvNets trained on DeepDRRs significantly outperformed ConvNets trained on naïve DRRs ([Formula: see text])., Conclusion: Our findings for both tasks are positive and promising. Combined with complementary approaches, such as image style transfer, the proposed framework for fast and realistic simulation of fluoroscopy from CT contributes to promoting the implementation of machine learning in X-ray-guided procedures. This paradigm shift has the potential to revolutionize intra-operative image analysis to simplify surgical workflows.

Purpose: Image-guided percutaneous interventions are safer alternatives to conventional orthopedic and trauma surgeries. To advance surgical tools in complex bony structures during these procedures with confidence, a large number of images is acquired. While image-guidance is the de facto standard to guarantee acceptable outcome, when these images are presented on monitors far from the surgical site the information content cannot be associated easily with the 3D patient anatomy., Methods: In this article, we propose a collaborative augmented reality (AR) surgical ecosystem to jointly co-localize the C-arm X-ray and surgeon viewer. The technical contributions of this work include (1) joint calibration of a visual tracker on a C-arm scanner and its X-ray source via a hand-eye calibration strategy, and (2) inside-out co-localization of human and X-ray observers in shared tracking and augmentation environments using vision-based simultaneous localization and mapping., Results: We present a thorough evaluation of the hand-eye calibration procedure. Results suggest convergence when using 50 pose pairs or more. The mean translation and rotation errors at convergence are 5.7 mm and [Formula: see text], respectively. Further, user-in-the-loop studies were conducted to estimate the end-to-end target augmentation error. The mean distance between landmarks in real and virtual environment was 10.8 mm., Conclusions: The proposed AR solution provides a shared augmented experience between the human and X-ray viewer. The collaborative surgical AR system has the potential to simplify hand-eye coordination for surgeons or intuitively inform C-arm technologists for prospective X-ray view-point planning.

Purpose: As the trend toward minimally invasive and percutaneous interventions continues, the importance of appropriate surgical data visualization becomes more evident. Ineffective interventional data display techniques that yield poor ergonomics that hinder hand-eye coordination, and therefore promote frustration which can compromise on-task performance up to adverse outcome. A very common example of ineffective visualization is monitors attached to the base of mobile C-arm X-ray systems., Methods: We present a spatially and imaging geometry-aware paradigm for visualization of fluoroscopic images using Interactive Flying Frustums (IFFs) in a mixed reality environment. We exploit the fact that the C-arm imaging geometry can be modeled as a pinhole camera giving rise to an 11-degree-of-freedom view frustum on which the X-ray image can be translated while remaining valid. Visualizing IFFs to the surgeon in an augmented reality environment intuitively unites the virtual 2D X-ray image plane and the real 3D patient anatomy. To achieve this visualization, the surgeon and C-arm are tracked relative to the same coordinate frame using image-based localization and mapping, with the augmented reality environment being delivered to the surgeon via a state-of-the-art optical see-through head-mounted display., Results: The root-mean-squared error of C-arm source tracking after hand-eye calibration was determined as [Formula: see text] and [Formula: see text] in rotation and translation, respectively. Finally, we demonstrated the application of spatially aware data visualization for internal fixation of pelvic fractures and percutaneous vertebroplasty., Conclusion: Our spatially aware approach to transmission image visualization effectively unites patient anatomy with X-ray images by enabling spatial image manipulation that abides image formation. Our proof-of-principle findings indicate potential applications for surgical tasks that mostly rely on orientational information such as placing the acetabular component in total hip arthroplasty, making us confident that the proposed augmented reality concept can pave the way for improving surgical performance and visuo-motor coordination in fluoroscopy-guided surgery.

Background: Periacetabular osteotomy (PAO) is the treatment of choice for younger patients with developmental hip dysplasia. The procedure aims to normalize the joint configuration, reduce the peak-pressure, and delay the development of osteoarthritis. The procedure is technically demanding and no previous study has validated the use of computer navigation with a minimally invasive transsartorial approach., Methods: Computer-assisted PAO was performed on ten patients. Patients underwent pre- and postoperative computed tomography (CT) scanning with a standardized protocol. Preoperative preparation consisted of outlining the lunate surface and segmenting the pelvis and femur from CT data. The Biomechanical Guidance System was used intra-operatively to automatically calculate diagnostic angles and peak-pressure measurements. Manual diagnostic angle measurements were performed based on pre- and postoperative CT. Differences in angle measurements were investigated with summary statistics, intraclass correlation coefficient, and Bland-Altman plots. The percentage postoperative change in peak-pressure was calculated., Results: Intra-operative reported angle measurements show a good agreement with manual angle measurements with intraclass correlation coefficient between 0.94 and 0.98. Computer navigation reported angle measurements were significantly higher for the posterior sector angle ([Formula: see text], [Formula: see text]) and the acetabular anteversion angle ([Formula: see text], [Formula: see text]). No significant difference was found for the center-edge ([Formula: see text]), acetabular index ([Formula: see text]), and anterior sector angle ([Formula: see text]). Peak-pressure after PAO decreased by a mean of 13% and was significantly different ([Formula: see text])., Conclusions: We found that computer navigation can reliably be used with a minimally invasive transsartorial approach PAO. Angle measurements generally agree with manual measurements and peak-pressure was shown to decrease postoperatively. With further development, the system will become a valuable tool in the operating room for both experienced and less experienced surgeons performing PAO. Further studies with a larger cohort and follow-up will allow us to investigate the association with peak-pressure and postoperative outcome and pave the way to clinical introduction.

Purpose: In minimally invasive interventions assisted by C-arm imaging, there is a demand to fuse the intra-interventional 2D C-arm image with pre-interventional 3D patient data to enable surgical guidance. The commonly used intensity-based 2D/3D registration has a limited capture range and is sensitive to initialization. We propose to utilize an opto/X-ray C-arm system which allows to maintain the registration during intervention by automating the re-initialization for the 2D/3D image registration. Consequently, the surgical workflow is not disrupted and the interaction time for manual initialization is eliminated., Methods: We utilize two distinct vision-based tracking techniques to estimate the relative poses between different C-arm arrangements: (1) global tracking using fused depth information and (2) RGBD SLAM system for surgical scene tracking. A highly accurate multi-view calibration between RGBD and C-arm imaging devices is achieved using a custom-made multimodal calibration target., Results: Several in vitro studies are conducted on pelvic-femur phantom that is encased in gelatin and covered with drapes to simulate a clinically realistic scenario. The mean target registration errors (mTRE) for re-initialization using depth-only and RGB [Formula: see text] depth are 13.23 mm and 11.81 mm, respectively. 2D/3D registration yielded 75% success rate using this automatic re-initialization, compared to a random initialization which yielded only 23% successful registration., Conclusion: The pose-aware C-arm contributes to the 2D/3D registration process by globally re-initializing the relationship of C-arm image and pre-interventional CT data. This system performs inside-out tracking, is self-contained, and does not require any external tracking devices.

Purpose: Patient-specific musculoskeletal biomechanical simulation is useful in preoperative surgical planning and postoperative assessment in orthopedic surgery and rehabilitation medicine. A difficulty in application of the patient-specific musculoskeletal modeling comes from the fact that the muscle attachment regions are typically invisible in CT and MRI. Our purpose is to develop a method for estimating patient-specific muscle attachment regions from 3D medical images and to validate with cadaver experiments., Methods: Eight fresh cadaver specimens of the lower extremity were used in the experiments. Before dissection, CT images of all the specimens were acquired and the bone regions in CT images were extracted using an automated segmentation method to reconstruct the bone shape models. During dissection, ten different muscle attachment regions were recorded with an optical motion tracker. Then, these regions obtained from eight cadavers were integrated on an average bone surface via non-rigid registration, and muscle attachment probabilistic atlases (PAs) were constructed. An average muscle attachment region derived from the PA was non-rigidly mapped to the patients bone surface to estimate the patient-specific muscle attachment region., Results: Average Dice similarity coefficient between the true and estimated attachment areas computed by the proposed method was more than 10% higher than the one computed by a previous method in most cases and the average boundary distance error of the proposed method was 1.1 mm smaller than the previous method on average., Conclusion: We conducted cadaver experiments to measure the attachment regions of the hip muscles and constructed PAs of the muscle attachment regions. The muscle attachment PA clarified the variations of the location of the muscle attachments and allowed us to estimate the patient-specific attachment area more accurately based on the patient bone shape derived from CT.

With thousands of organic chemicals released every day into our environment, Europe and other continents are confronted with increased risk of health and environmental problems. Even if a strict regulation such as REgistration, Authorization and restriction of CHemicals (REACH) is imposed and followed by industry to ensure that they prove the harmlessness of their substances, not all testing procedures are designed to cope with the complexity of the environment. This is especially true for the evaluation of persistence through biodegradability assessment guidelines. Our new approach has been to adapt "in the lab" biodegradability assessment to the environmental conditions and model the probability for a biodegradation test to be positive in the form of a logistic function of both the temperature and the viable cell density. Here, a proof of this new concept is proposed with the establishment of tri-dimensional biodegradability profiles of six chemicals (sodium benzoate, 4-nitrophenol, diethylene glycol, 2,4,5-trichlorophenol, atrazine, and glyphosate) between 4 to 30 °C and 10(4) to 10(8) cells ml(-1) as can be found in environmental compartments in time and space. The results show a significant increase of the predictive power of existing screening lab-scale tests designed for soluble substances. This strategy can be complementary to those current testing strategies with the creation of new indicators to quantify environmental persistence using lab-scale tests.

Purpose: C-arm radiographs are commonly used for intraoperative image guidance in surgical interventions. Fluoroscopy is a cost-effective real-time modality, although image quality can vary greatly depending on the target anatomy. Cone-beam computed tomography (CBCT) scans are sometimes available, so 2D-3D registration is needed for intra-procedural guidance. C-arm radiographs were registered to CBCT scans and used for 3D localization of peritumor fiducials during a minimally invasive thoracic intervention with a da Vinci Si robot., Methods: Intensity-based 2D-3D registration of intraoperative radiographs to CBCT was performed. The feasible range of X-ray projections achievable by a C-arm positioned around a da Vinci Si surgical robot, configured for robotic wedge resection, was determined using phantom models. Experiments were conducted on synthetic phantoms and animals imaged with an OEC 9600 and a Siemens Artis zeego, representing the spectrum of different C-arm systems currently available for clinical use., Results: The image guidance workflow was feasible using either an optically tracked OEC 9600 or a Siemens Artis zeego C-arm, resulting in an angular difference of Δθ:∼ 30°. The two C-arm systems provided TRE mean ≤ 2.5 mm and TRE mean ≤ 2.0 mm, respectively (i.e., comparable to standard clinical intraoperative navigation systems)., Conclusions: C-arm 3D localization from dual 2D-3D registered radiographs was feasible and applicable for intraoperative image guidance during da Vinci robotic thoracic interventions using the proposed workflow. Tissue deformation and in vivo experiments are required before clinical evaluation of this system.

Purpose: Le Fort-based face-jaw-teeth transplantation (FJTT) attempts to marry bone and teeth geometry of size-mismatched face-jaw-teeth segments to restore function and form due to severe mid-facial trauma. Recent development of a computer-assisted planning and execution (CAPE) system for Le Fort-based FJTT in a pre-clinical swine model offers preoperative planning, and intraoperative navigation. This paper addresses the translation of the CAPE system to human anatomy and presents accuracy results., Methods: Single-jaw, Le Fort-based FJTTs were performed on plastic models, one swine and one human, and on a human cadaver. Preoperative planning defined the goal placement of the donor's Le Fort-based FJTT segment on the recipient. Patient-specific navigated cutting guides helped achieve planned osteotomies. Intraoperative cutting guide and donor fragment placement were compared with postoperative computed tomography (CT) data and the preoperative plan., Results: Intraoperative measurement error with respect to postoperative CT was less than 1.25 mm for both mock transplants and 3.59 mm for the human cadaver scenario. Donor fragment placement (as compared to the planned position) was less accurate for the human model test case (2.91 mm) compared with the swine test (2.25 mm) and human cadaver (2.26 mm)., Conclusion: The results indicate the viability of the CAPE system for assisting with Le Fort-based FJTT and demonstrate the potential in human surgery. This system offers a new path forward to achieving improved outcomes in Le Fort-based FJTT and can be modified to assist with a variety of other surgeries involving the head, neck, face, jaws and teeth.

Purpose: This paper presents and validates a computer-navigated system for performing periacetabular osteotomy (PAO) to treat developmental dysplasia of the hip. The main motivation of the biomechanical guidance system (BGS) is to plan and track the osteotomy fragment in real time during PAO while simplifying the procedure for less-experienced surgeons. The BGS aims at developing a platform for comparing biomechanical states of the joint with the current gold standard geometric assessment of anatomical angles. The purpose of this study was to (1) determine the accuracy with which the BGS tracks the hip joint through repositioning and (2) identify improvements to the workflow., Methods: Nineteen cadaveric validation studies quantified system accuracy, verified system application, and helped to refine surgical protocol. In two surgeries, navigation and registration accuracy were computed by affixing fiducials to two cadavers prior to surgery. All scenarios compared anatomical angle measurements and joint positioning as measured intraoperatively to postoperatively., Results: In the two cases with fiducials, computed fragment transformations deviated from measured fiducial transformations by 1.4 and 1.8 mm in translation and 1.0° and 2.2° in rotation, respectively. The additional seventeen surgeries showed strong agreement between intraoperative and postoperative anatomical angles, helped to refine the surgical protocol, and demonstrated system robustness., Conclusion: Estimated accuracy with BGS appeared acceptable for future surgical applications. Several major system requirements were identified and addressed, improving the BGS and making it feasible for clinical studies.

Since the start of 2010 there has been a flare-up of measles in France, following on the resurgence observed in 2008. The aim of this study was to present results of the epidemiological surveillance of measles in the French armed forces and to describe the increase in incidence. Measles was surveyed from 1992 to 2010. Criteria for report were those used for French national compulsory notification. The data, concerning active military personnel, were provided by the physicians in the armed forces using anonymous data collection forms. Between 1992 and July 2010, 689 cases of measles were notified. Since 2002, the mean incidence rate was 1 case per 100,000. A significant increase has been observed for 2010 (13.9 cases per 100,000 in 2010 versus 1.8 in 2009). The 28 cases reported in 2010 involved five clusters and three isolated cases. The mean age of affected subjects was 27 years. Only 30% of cases had been vaccinated. The epidemic resurgence of measles observed in 2010 in the French armed forces follows the same pattern as that observed nationally and at European level, and can be seen as the likely consequence of inadequate vaccination cover.