pyM2aia: Python interface for mass spectrometry imaging with focus on deep learning.

SUMMARY: Python is the most commonly used language for deep learning (DL). Existing Python packages for mass spectrometry imaging (MSI) data are not optimized for DL tasks. We, therefore, introduce pyM2aia, a Python package for MSI data analysis with a focus on memory-efficient handling, processing and convenient data-access for DL applications. pyM2aia provides interfaces to its parent application M2aia, which offers interactive capabilities for exploring and annotating MSI data in imzML format. pyM2aia utilizes the image input and output routines, data formats, and processing functions of M2aia, ensures data interchangeability, and enables the writing of readable and easy-to-maintain DL pipelines by providing batch generators for typical MSI data access strategies. We showcase the package in several examples, including imzML metadata parsing, signal processing, ion-image generation, and, in particular, DL model training and inference for spectrum-wise approaches, ion-image-based approaches, and approaches that use spectral and spatial information simultaneously. AVAILABILITY AND IMPLEMENTATION: Python package, code and examples are available at (https://m2aia.github.io/m2aia).

Surgical phase and instrument recognition: how to identify appropriate dataset splits.

PURPOSE: Machine learning approaches can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes. Surgical workflow and instrument recognition are two tasks that are complicated in this manner, because of heavy data imbalances resulting from different length of phases and their potential erratic occurrences. Furthermore, sub-properties like instrument (co-)occurrence are usually not particularly considered when defining the split. METHODS: We present a publicly available data visualization tool that enables interactive exploration of dataset partitions for surgical phase and instrument recognition. The application focuses on the visualization of the occurrence of phases, phase transitions, instruments, and instrument combinations across sets. Particularly, it facilitates assessment of dataset splits, especially regarding identification of sub-optimal dataset splits. RESULTS: We performed analysis of the datasets Cholec80, CATARACTS, CaDIS, M2CAI-workflow, and M2CAI-tool using the proposed application. We were able to uncover phase transitions, individual instruments, and combinations of surgical instruments that were not represented in one of the sets. Addressing these issues, we identify possible improvements in the splits using our tool. A user study with ten participants demonstrated that the participants were able to successfully solve a selection of data exploration tasks. CONCLUSION: In highly unbalanced class distributions, special care should be taken with respect to the selection of an appropriate dataset split because it can greatly influence the assessments of machine learning approaches. Our interactive tool allows for determination of better splits to improve current practices in the field. The live application is available at https://cardio-ai.github.io/endovis-ml/ .

Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer.

Prostate cancer (PCa) diagnosis on multi-parametric magnetic resonance images (MRI) requires radiologists with a high level of expertise. Misalignments between the MRI sequences can be caused by patient movement, elastic soft-tissue deformations, and imaging artifacts. They further increase the complexity of the task prompting radiologists to interpret the images. Recently, computer-aided diagnosis (CAD) tools have demonstrated potential for PCa diagnosis typically relying on complex co-registration of the input modalities. However, there is no consensus among research groups on whether CAD systems profit from using registration. Furthermore, alternative strategies to handle multi-modal misalignments have not been explored so far. Our study introduces and compares different strategies to cope with image misalignments and evaluates them regarding to their direct effect on diagnostic accuracy of PCa. In addition to established registration algorithms, we propose 'misalignment augmentation' as a concept to increase CAD robustness. As the results demonstrate, misalignment augmentations can not only compensate for a complete lack of registration, but if used in conjunction with registration, also improve the overall performance on an independent test set.

Comparative evaluation of three commercially available markerless depth sensors for close-range use in surgical simulation.

PURPOSE: Minimally invasive surgeries have restricted surgical ports, demanding a high skill level from the surgeon. Surgical simulation potentially reduces this steep learning curve and additionally provides quantitative feedback. Markerless depth sensors show great promise for quantification, but most such sensors are not designed for accurate reconstruction of complex anatomical forms in close-range. METHODS: This work compares three commercially available depth sensors, namely the Intel D405, D415, and the Stereolabs Zed-Mini in the range of 12-20 cm, for use in surgical simulation. Three environments are designed that closely mimic surgical simulation, comprising planar surfaces, rigid objects, and mitral valve models of silicone and realistic porcine tissue. The cameras are evaluated on Z-accuracy, temporal noise, fill rate, checker distance, point cloud comparisons, and visual inspection of surgical scenes, across several camera settings. RESULTS: The Intel cameras show sub-mm accuracy in most static environments. The D415 fails in reconstructing valve models, while the Zed-Mini provides lesser temporal noise and higher fill rate. The D405 could reconstruct anatomical structures like the mitral valve leaflet and a ring prosthesis, but performs poorly for reflective surfaces like surgical tools and thin structures like sutures. CONCLUSION: If a high temporal resolution is needed and lower spatial resolution is acceptable, the Zed-Mini is the best choice, whereas the Intel D405 is the most suited for close-range applications. The D405 shows potential for applications like deformable registration of surfaces, but is not yet suitable for applications like real-time tool tracking or surgical skill assessment.

Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging.

Mass spectrometry imaging vows to enable simultaneous spatially resolved investigation of hundreds of metabolites in tissues, but it primarily relies on traditional ion images for non-data-driven metabolite visualization and analysis. The rendering and interpretation of ion images neither considers nonlinearities in the resolving power of mass spectrometers nor does it yet evaluate the statistical significance of differential spatial metabolite abundance. Here, we outline the computational framework moleculaR ( https://github.com/CeMOS-Mannheim/moleculaR ) that is expected to improve signal reliability by data-dependent Gaussian-weighting of ion intensities and that introduces probabilistic molecular mapping of statistically significant nonrandom patterns of relative spatial abundance of metabolites-of-interest in tissue. moleculaR also enables cross-tissue statistical comparisons and collective molecular projections of entire biomolecular ensembles followed by their spatial statistical significance evaluation on a single tissue plane. It thereby fosters the spatially resolved investigation of ion milieus, lipid remodeling pathways, or complex scores like the adenylate energy charge within the same image.

M2aia-Interactive, fast, and memory-efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data.

BACKGROUND: Mass spectrometry imaging (MSI) is a label-free analysis method for resolving bio-molecules or pharmaceuticals in the spatial domain. It offers unique perspectives for the examination of entire organs or other tissue specimens. Owing to increasing capabilities of modern MSI devices, the use of 3D and multi-modal MSI becomes feasible in routine applications-resulting in hundreds of gigabytes of data. To fully leverage such MSI acquisitions, interactive tools for 3D image reconstruction, visualization, and analysis are required, which preferably should be open-source to allow scientists to develop custom extensions. FINDINGS: We introduce M2aia (MSI applications for interactive analysis in MITK), a software tool providing interactive and memory-efficient data access and signal processing of multiple large MSI datasets stored in imzML format. M2aia extends MITK, a popular open-source tool in medical image processing. Besides the steps of a typical signal processing workflow, M2aia offers fast visual interaction, image segmentation, deformable 3D image reconstruction, and multi-modal registration. A unique feature is that fused data with individual mass axes can be visualized in a shared coordinate system. We demonstrate features of M2aia by reanalyzing an N-glycan mouse kidney dataset and 3D reconstruction and multi-modal image registration of a lipid and peptide dataset of a mouse brain, which we make publicly available. CONCLUSIONS: To our knowledge, M2aia is the first extensible open-source application that enables a fast, user-friendly, and interactive exploration of large datasets. M2aia is applicable to a wide range of MSI analysis tasks.

Unsupervised Domain Adaptation From Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks.

Anisotropic multi-slice Cardiac Magnetic Resonance (CMR) Images are conventionally acquired in patient-specific short-axis (SAX) orientation. In specific cardiovascular diseases that affect right ventricular (RV) morphology, acquisitions in standard axial (AX) orientation are preferred by some investigators, due to potential superiority in RV volume measurement for treatment planning. Unfortunately, due to the rare occurrence of these diseases, data in this domain is scarce. Recent research in deep learning-based methods mainly focused on SAX CMR images and they had proven to be very successful. In this work, we show that there is a considerable domain shift between AX and SAX images, and therefore, direct application of existing models yield sub-optimal results on AX samples. We propose a novel unsupervised domain adaptation approach, which uses task-related probabilities in an attention mechanism. Beyond that, cycle consistency is imposed on the learned patient-individual 3D rigid transformation to improve stability when automatically re-sampling the AX images to SAX orientations. The network was trained on 122 registered 3D AX-SAX CMR volume pairs from a multi-centric patient cohort. A mean 3D Dice of 0.86 ± 0.06 for the left ventricle, 0.65 ± 0.08 for the myocardium, and 0.77 ± 0.10 for the right ventricle could be achieved. This is an improvement of 25% in Dice for RV in comparison to direct application on axial slices. To conclude, our pre-trained task module has neither seen CMR images nor labels from the target domain, but is able to segment them after the domain gap is reduced. Code: https://github.com/Cardio-AI/3d-mri-domain-adaptation.

Computational Analysis of Alzheimer Amyloid Plaque Composition in 2D- and Elastically Reconstructed 3D-MALDI MS Images.

MALDI mass spectrometry imaging (MSI) enables label-free, spatially resolved analysis of a wide range of analytes in tissue sections. Quantitative analysis of MSI datasets is typically performed on single pixels or manually assigned regions of interest (ROIs). However, many sparse, small objects such as Alzheimer's disease (AD) brain deposits of amyloid peptides called plaques are neither single pixels nor ROIs. Here, we propose a new approach to facilitate the comparative computational evaluation of amyloid plaque-like objects by MSI: a fast PLAQUE PICKER tool that enables a statistical evaluation of heterogeneous amyloid peptide composition. Comparing two AD mouse models, APP NL-G-F and APP PS1, we identified distinct heterogeneous plaque populations in the NL-G-F model but only one class of plaques in the PS1 model. We propose quantitative metrics for the comparison of technical and biological MSI replicates. Furthermore, we reconstructed a high-accuracy 3D-model of amyloid plaques in a fully automated fashion, employing rigid and elastic MSI image registration using structured and plaque-unrelated reference ion images. Statistical single-plaque analysis in reconstructed 3D-MSI objects revealed the Aß1-42Arc peptide to be located either in the core of larger plaques or in small plaques without colocalization of other Aß isoforms. In 3D, a substantially larger number of small plaques were observed than that indicated by the 2D-MSI data, suggesting that quantitative analysis of molecularly diverse sparsely-distributed features may benefit from 3D-reconstruction. Data are available via ProteomeXchange with identifier PXD020824.

Flexible and comprehensive patient-specific mitral valve silicone models with chordae tendineae made from 3D-printable molds.

PURPOSE: Given the multitude of challenges surgeons face during mitral valve repair surgery, they should have a high confidence in handling of instruments and in the application of surgical techniques before they enter the operating room. Unfortunately, opportunities for surgical training of minimally invasive repair are very limited, leading to a situation where most surgeons undergo a steep learning curve while operating the first patients. METHODS: In order to provide a realistic tool for surgical training, a commercial simulator was augmented by flexible patient-specific mitral valve replica. In an elaborated production pipeline, finalized after many optimization cycles, models were segmented from 3D ultrasound and then 3D-printable molds were computed automatically and printed in rigid material, the lower part being water-soluble. After silicone injection, the silicone model was dissolved from the mold and anchored in the simulator. RESULTS: To our knowledge, our models are the first to comprise the full mitral valve apparatus, i.e., the annulus, leaflets, chordae tendineae and papillary muscles. Nine different valve molds were automatically created according to the proposed workflow (seven prolapsed valves and two valves with functional mitral insufficiency). From these mold geometries, 16 replica were manufactured. A material test revealed that EcoflexTM 00-30 is the most suitable material for leaflet-mimicking tissue out of seven mixtures. Production time was around 36 h per valve. Twelve surgeons performed various surgical techniques, e.g., annuloplasty, neo-chordae implantation, triangular leaflet resection, and assessed the realism of the valves very positively. CONCLUSION: The standardized production process guarantees a high anatomical recapitulation of the silicone valves to the segmented models and the ultrasound data. Models are of unprecedented quality and maintain a high realism during haptic interaction with instruments and suture material.

Replicated mitral valve models from real patients offer training opportunities for minimally invasive mitral valve repair.

OBJECTIVES: Minimally invasive mitral valve repair is considered a challenging procedure. Mastering the necessary skills takes years of training and clinical experience. To date, reconstructive surgery is performed mainly by a few surgeons with a strong track record, whereas trainees have only limited opportunities to practise. METHODS: A high-fidelity training simulator was equipped with novel silicone replicas of patient-specific mitral valves containing all of the anatomical components of the valve. The goal of this system was to aid members of the surgical community to overcome the steep learning curve. RESULTS: Twelve surgeons (5 experts and 7 surgical resident trainees) performed a minimally invasive mitral valve repair procedure on these models and assessed the usefulness for different applications. The trainees found the main application to be general surgical training and education for mitral valve repair, whereas the experts found the main benefit to be rehearsal for a specific patient. The skills of the trainees were improved in only a single session. The valve models placed in a water solution showed a high echogenicity. CONCLUSIONS: Preoperative patient-specific simulation could improve the safety and effectiveness of mitral valve repair in the hands of a larger number of surgeons. Because the system is based on a quantitative segmentation of the anatomy of the mitral valve, it offers young surgeons training in general dexterity and also provides an exact numerical quantitative assessment of valvular geometry. This system can be used to educate surgeons to strive for and achieve well-defined and measurable surgical changes to the anatomy of the valve and to achieve the desired functional results.

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques.

In biological assays, automated cell/colony segmentation and counting is imperative owing to huge image sets. Problems occurring due to drifting image acquisition conditions, background noise and high variation in colony features in experiments demand a user-friendly, adaptive and robust image processing/analysis method. We present AutoCellSeg (based on MATLAB) that implements a supervised automatic and robust image segmentation method. AutoCellSeg utilizes multi-thresholding aided by a feedback-based watershed algorithm taking segmentation plausibility criteria into account. It is usable in different operation modes and intuitively enables the user to select object features interactively for supervised image segmentation method. It allows the user to correct results with a graphical interface. This publicly available tool outperforms tools like OpenCFU and CellProfiler in terms of accuracy and provides many additional useful features for end-users.

Computer-based comparison of different methods for selecting mitral annuloplasty ring size.

BACKGROUND: Ring sizing for mitral valve annuloplasty is conventionally done intraoperatively using specific 'sizer' instruments, which are placed onto the valve tissue. This approach is barely reproducible since different sizing strategies have been established among surgeons. The goal of this study is to virtually apply different sizing methods on the basis of pre-repair echocardiography to find out basic differences between sizing strategies. METHODS: In three-dimensional echocardiographs of 43 patients, the mitral annulus and the contour of the anterior mitral leaflet were segmented using MITK Mitralyzer software. Similarly, three-dimensional virtual models of Carpentier-Edwards Physio II annuloplasty rings and their corresponding sizers were interactively generated from computer tomography images. For each patient, the matching annuloplasty ring was selected repeatedly according to popular sizing strategies, such as the height of anterior mitral leaflet, the intercommissural distance and the surface area of anterior mitral leaflet. The areas of the selected rings were considered as the neo-surface area of the mitral annulus after implantation. RESULTS: The sizing of the mitral valve according to the height of anterior mitral leaflet (mean ring size = 29.9 ± 3.90), intercommissural distance (mean ring size = 37.5 ± 1.92) or surface area of anterior mitral leaflet (mean ring size = 32.7 ± 3.3) led to significantly different measurements (p ≤ 0.01). In contrary to intercommissural distance, height and surface area of the anterior mitral leaflet exhibited significant variations between the patients (p ≤ 0.01). The sizing according to the height of anterior mitral leaflet led to the maximal reduction of the mitral annulus surface area followed by the sizing according to the surface area of anterior mitral leaflet and finally by the intercommissural distance. CONCLUSIONS: This novel comprehensive computer-based analysis reveals that the surveyed sizing methods led to the selection of significantly different annuloplasty rings and therefore underscore the ambiguity of routinely applied annuloplasty sizing strategies.

The geometrical effect of different annuloplasty rings on mitral valve annulus.

BACKGROUND: Different types of mitral annuloplasty rings are commercially available. The aim of this study was to investigate the effect of implantation of six types of annuloplasty rings on the geometry and dynamics of the mitral valve. METHODS: Three-dimensional echocardiography images of 42 patients were acquired to visualize the mitral valve annulus. Virtual representations of six commercially available annuloplasty rings were matched to anatomical mitral annuli of each patient according to anterolateral-posteromedial diameter. The virtual displacement of each annuloplasty ring after the implantation was measured and compared with the other rings. RESULTS: Patients with severe mitral regurgitation had significantly dilated annuli according to anterolateral-posteromedial diameter, anterior-posterior diameter and to annulus circumference. Anterior and posterior heights of the mitral annuli and non-planarity angle showed no significant differences among different patients with different degree of mitral regurgitation. The ratio of anterior-posterior to anterolateral-posteromedial diameter was almost identical in all groups with identical annular shapes. The implantation of the Carpentier-Edwards Classic Annuloplasty Ring™ led to maximal displacement of mitral annulus, followed by the IM-Ring™, without a statistical significance. In contrary, the implantation of a MyxoETlogix Ring™ was associated with minimal displacement of mitral annulus throughout the groups, but without statistical significance. CONCLUSIONS: The implantation of different ring types in patients with different annuli shapes and dimensions did not lead to any significant change in the configuration of mitral annuli after the virtual implantation of the tested annuloplasty rings.

Intraoperative Quantitative Mitral Valve Analysis Using Optical Tracking Technology.

PURPOSE: Analysis of mitral valve morphology during reconstruction is routinely based on visual assessment and subjective, poorly reproducible measurements. We prove the feasibility of a new intraoperative system for quantitative mitral valve analysis. DESCRIPTION: The proposed computer-based assistance system enables accurate intraoperative localization of anatomic landmarks on the mitral valve apparatus using optical tracking technology. Measurement and visualization strategies were specifically developed and tailored for mitral valve operations. EVALUATION: The feasibility of intraoperative quantitative measurements was successfully shown for 9 patients. Precise geometric descriptions of the valve were obtained and adequately visualized, providing valuable decision support during the intervention. The mean annular area obtained from the intraoperative measurements was 736 ± 266 mm(2), in good agreement with the mean area of the implanted prosthetic rings of 617 ± 124 mm(2), which are slightly smaller due to annular downsizing. Comparison with preoperative three-dimensional echocardiography revealed differences between the beating heart, with transverse and septolateral annular diameters of 40.6 ± 15.4 mm and 41.2 ± 8.2 mm, and the intraoperative cardioplegic condition, with corresponding diameters of 34.3 ± 6.9 mm and 27.4 ± 5.6 mm. CONCLUSIONS: Mitral valve analysis by optical tracking represents a unique technologic advance in intraoperative assessment, providing the surgeon with an extended quantitative perception of surgical target. This technology promotes a major philosophical change from an empirical procedure toward a quantitatively predictable modern reconstructive operation.

Articular surface segmentation using active shape models for intraoperative implant assessment.

PURPOSE: In orthopedic surgeries, it is important to avoid intra-articular implant placements, which increase revision rates and the risk of arthritis. In order to support the intraoperative assessment and correction of surgical implants, we present an automatic detection approach using cone-beam computed tomography (CBCT). METHODS: Multiple active shape models (ASM) with specified articular surface regions are used to isolate the joint spaces. Fast and easy-to-implement methods are integrated in the ASM segmentation to optimize the robustness and accuracy for intraoperative application. A cylinder detection method is applied to determine metal implants. Intersections between articular surfaces and cylinders are detected and used to find intra-articular collisions. RESULTS: Segmentations of two calcaneal articular surfaces were evaluated on 50 patient images and have shown average surface distance errors of 0.59 and 0.46 mm, respectively. The proposed model-independent segmentation at the specified articular surface regions allowed to significantly decrease the error by 22 and 25 % on average. The method was able to compensate suboptimal initializations for translations of up to 16 mm and rotations of up to 21[Formula: see text]. In a human cadaver test, articular perforations could be localized with an accuracy of 0.80 mm on average. CONCLUSIONS: A concept for automatic intraoperative detection of intra-articular implants in CBCT images was presented. The results show a reliable segmentation of articular surfaces in retrospective patient data and an accurate localization of misplaced implants in artificially created human cadaver test cases.

Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI.

Effective image-based artifact correction is an essential step in the analysis of diffusion MR images. Many current approaches are based on retrospective registration, which becomes challenging in the realm of high b -values and low signal-to-noise ratio, rendering the corresponding correction schemes more and more ineffective. We propose a novel registration scheme based on memetic search optimization that allows for simultaneous exploitation of different signal intensity relationships between the images, leading to more robust registration results. We demonstrate the increased robustness and efficacy of our method on simulated as well as in vivo datasets. In contrast to the state-of-art methods, the median target registration error (TRE) stayed below the voxel size even for high b -values (3000 s ·mm-2 and higher) and low SNR conditions. We also demonstrate the increased precision in diffusion-derived quantities by evaluating Neurite Orientation Dispersion and Density Imaging (NODDI) derived measures on a in vivo dataset with severe motion artifacts. These promising results will potentially inspire further studies on metaheuristic optimization in diffusion MRI artifact correction and image registration in general.

Accuracy evaluation of a mitral valve surgery assistance system based on optical tracking.

PURPOSE: Mitral valve reconstruction is a widespread surgical method to repair incompetent mitral valves, which usually includes implantation of a ring prosthesis. To date, intraoperative analysis of the mitral valve is merely based on visual assessment using simple surgical tools, which might not allow for accurate assessment of the complex anatomy. METHODS: We propose a novel intraoperative computer-based assistance system, which combines passive optical tracking technology with tailored measurement strategies applicable during different phases of the intraoperative workflow. Based on the assessment of the valvular apparatus by customized tracked instruments, the system (1) generates an enhanced three-dimensional visualization, which (2) incorporates accurate quantifications and (3) provides assistance, e.g., in terms of virtual prosthesis selection. RESULTS: Phantom experiments in a realistic environment revealed a high system accuracy (mean precision [Formula: see text] mm and mean trueness [Formula: see text] mm) and a low user error (mean precision [Formula: see text] mm and mean trueness [Formula: see text] mm). The assistance system was successfully applied five times during open and minimally invasive reconstructive surgery in patients having mitral valve insufficiency. The measurement steps integrate well into the traditional workflow, enhancing the surgeon's three-dimensional perception and generating a suggestion for an appropriate prosthesis. CONCLUSION: The proposed assistance system provides a novel, accurate, and reproducible method for assessing the valvular geometry intraoperatively.

Intra-operative adjustment of standard planes in C-arm CT image data.

PURPOSE: With the help of an intra-operative mobile C-arm CT, medical interventions can be verified and corrected, avoiding the need for a post-operative CT and a second intervention. An exact adjustment of standard plane positions is necessary for the best possible assessment of the anatomical regions of interest but the mobility of the C-arm causes the need for a time-consuming manual adjustment. In this article, we present an automatic plane adjustment at the example of calcaneal fractures. METHODS: We developed two feature detection methods (2D and pseudo-3D) based on SURF key points and also transferred the SURF approach to 3D. Combined with an atlas-based registration, our algorithm adjusts the standard planes of the calcaneal C-arm images automatically. The robustness of the algorithms is evaluated using a clinical data set. Additionally, we tested the algorithm's performance for two registration approaches, two resolutions of C-arm images and two methods for metal artifact reduction. RESULTS: For the feature extraction, the novel 3D-SURF approach performs best. As expected, a higher resolution ([Formula: see text] voxel) leads also to more robust feature points and is therefore slightly better than the [Formula: see text] voxel images (standard setting of device). Our comparison of two different artifact reduction methods and the complete removal of metal in the images shows that our approach is highly robust against artifacts and the number and position of metal implants. CONCLUSIONS: By introducing our fast algorithmic processing pipeline, we developed the first steps for a fully automatic assistance system for the assessment of C-arm CT images.

Intra-operative assessment of fractured articular surfaces in cone beam CT image data.

PURPOSE: The assessment of intra-operatively acquired volumetric data is a difficult and often time-consuming task, which demands a new set of skills from the surgeons. In the case of orthopedic surgeries such as the treatment of calcaneal fractures, the correctness of the reduction of the bone fragments can be verified with the help of C-arm CT volumetric images. For an accurate intra-operative assessment of the displaced fragments, an automatic segmentation of the articular surfaces and color-coded visualization was developed. METHODS: Our automatic approach consists of three major steps: first, using adjusted standard planes intersecting the articular region, the joint space is localized with an intensity profile-based method. In a second step, the localized joint space is segmented on the Laplacian of Gaussian filtered volumetric image by a modified binary flood fill algorithm. Finally, a 3D surface model of the segmented joint space is analyzed and visualized with focus on critical displacements of the surface. RESULTS: A specifically designed human cadaver study consisting of ten lower legs of ten different donors was conducted to acquire 48 realistic C-arm CT images of misaligned bone fragments (steps of varying sizes) in the posterior talar articular surface of the calcaneus. The proposed algorithmic pipeline was verified by the acquired image data and showed very good results with no false positives and an overall correct displacement assessment of 93.8%. CONCLUSIONS: The proposed algorithmic pipeline can be easily integrated into the clinical workflow and qualifies for intra-operative usage. It showed very good results on the reference data set of the cadaver study. With the help of such an assistance system, the time-consuming process of 2D view adjustment and visual assessment of the gray value images can be greatly simplified.