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Objective Magnetic resonance imaging is the standard imaging modality to assess articular cartilage. As the imaging surrogate of degenerative joint disease, cartilage thickness is commonly quantified after tissue segmentation. In lack of a standard method, this study systematically compared five methods for automatic cartilage thickness measurements across the knee joint and as a function of region and sub-region: 3D mesh normals (3D-MN), 3D nearest neighbors (3D-NN), 3D ray tracing (3D-RT), 2D centerline normals (2D-CN), and 2D surface normals (2D-SN). Design Based on the manually segmented femoral and tibial cartilage of 507 human knee joints, mean cartilage thickness was computed for the entire femorotibial joint, 4 joint regions, and 20 subregions using these methods. Inter-method comparisons of mean cartilage thickness and computation times were performed by one-way analysis of variance (ANOVA), Bland-Altman analyses and Lin’s concordance correlation coefficient (CCC). Results Mean inter-method differences in cartilage thickness were significant in nearly all subregions ( P < 0.001). By trend, mean differences were smallest between 3D-MN and 2D-SN in most (sub)regions, which is also reflected by highest quantitative inter-method agreement and CCCs. 3D-RT was prone to severe overestimation of up to 2.5 mm. 3D-MN, 3D-NN, and 2D-SN required mean processing times of ≤5.3 s per joint and were thus similarly efficient, whereas the time demand of 2D-CN and 3D-RT was much larger at 133 ± 29 and 351 ± 10 s per joint ( P < 0.001). Conclusions In automatic cartilage thickness determination, quantification accuracy and computational burden are largely affected by the underlying method. Mesh and surface normals or nearest neighbor searches should be used because they accurately capture variable geometries while being time-efficient.

Scientific reports 10, 15106 (2020). doi:10.1038/s41598-020-72208-y, Published by Macmillan Publishers Limited, part of Springer Nature, [London]

Objective Beyond static assessment, functional techniques are increasingly applied in magnetic resonance imaging (MRI) studies. Stress MRI techniques bring together MRI and mechanical loading to study knee joint and tissue functionality, yet prototypical axial compressive loading devices are bulky and complex to operate. This study aimed to design and validate an MRI-compatible pressure-controlled varus–valgus loading device that applies loading along the joint line. Methods Following the device’s thorough validation, we demonstrated proof of concept by subjecting a structurally intact human cadaveric knee joint to serial imaging in unloaded and loaded configurations, i.e. to varus and valgus loading at 7.5 kPa (= 73.5 N), 15 kPa (= 147.1 N), and 22.5 kPa (= 220.6 N). Following clinical standard (PDw fs) and high-resolution 3D water-selective cartilage (WATSc) sequences, we performed manual segmentations and computations of morphometric cartilage measures. We used CT and radiography (to quantify joint space widths) and histology and biomechanics (to assess tissue quality) as references. Results We found (sub)regional decreases in cartilage volume, thickness, and mean joint space widths reflective of areal pressurization of the medial and lateral femorotibial compartments. Discussion Once substantiated by larger sample sizes, varus–valgus loading may provide a powerful alternative stress MRI technique.

Thermal infrared imaging is an emerging modality that has gained increasing interest in recent years, mostly due to technical advances resulting in the availability of affordable microbolometer-based IR imaging sensors. However, while sensors are widely available, algorithms for thermal image processing still lack robustness and accuracy when compared to their RGB counterparts. Current methods developed for RGB data make use of machine learning algorithms that require large amounts of labeled images which are currently not available for the thermal domain. In this paper, we address the question whether providing a large number of labeled images would allow the application of current image processing methods on the example of solving challenging face analysis tasks. We introduce a high-resolution thermal facial image database with extensive manual annotations and explore how it can be used to adapt methods from the visual domain for infrared images. In addition, we extend existing approaches for infrared landmark detection with a head pose estimation for improved robustness and analyze the performance of a deep learning method on this task. An evaluation of algorithm performance shows that learning algorithms either outperform available solutions or allow completely new applications that could previously not be addressed. As a conclusion, we prove that investing the effort into acquiring appropriate training data and adapting competitive algorithms is not only a viable approach in analysing thermal infrared images but can also allow outperforming specific task-designed solutions. The database is freely available for academic use at https://github.com/marcinkopaczka/thermalfaceproject .

Background and Objective Segmentation on carpus provides essential information for clinical applications including pathological evaluations, therapy planning, wrist biomechanical analysis, etc. Along with the acquisition procedure of magnetic resonance (MR) technique, poor quality of wrist images (e.g., occlusion, low signal-to-noise ratio, and contrast) often causes segmentation failure. Methods In this work, to address such problems, a shape prior enhanced level set model was proposed. By transferring a shape contour in Cartesian Coordinate System (COS) into a curve in Polar Coordinate System (POS), parameters describing conventional shape invariance, i.e., translations, rotation, and scale were simplified into a single parameter for phase shift, which strongly improved algorithm efficiency. Given a training set in COS, a confidence interval representing the corresponding curves in POS was utilized as the shape prior set term in the model. Integrated with an edge detector, a local intensity descriptor, and a regularization term, the proposed method further possessed abilities against noise, intensity inhomogeneity as well as re-initialization problem. Images from 15 in-vivo acquired MR-datasets of the human wrist were used for validation. The performance of the proposed method has been compared with three state-of-the-art methods. Results We reported a Dice Similarity Coefficient of 96.88±1.20%, a Relative Volume Difference of -1.53±3.01%, a Volume Overlap Error of 6.03±2.23%, a 95% Hausdorff Distance of 1.43±0.66 mm, an Average Symmetric Surface Distance of 0.50±0.17 mm, and a Root Mean Square Distance of 0.71±0.25 mm for the proposed method. The time consumption was 36.03±19.98 s. Conclusions Experimental results indicated that, compared with three other methods, the proposed method achieved significant improvement in terms of accuracy and efficiency.

Monthly notices of the Royal Astronomical Society 503(1), 574-585 (2021). doi:10.1093/mnras/stab427, Published by Oxford Univ. Press, Oxford

Face alignment is commonly solved by utilizing a shape representation which is either implicitly learned as in current deep architectures or explicitly given as in active shape models and their derivatives. In this work, we present a method for integrating explicit statistical shape priors into a deep convolutional network by introducing a novel layer which allows using statistical PCA-based models directly in the network, resulting in a highly efficient deep face alignment architecture capable of performing landmark detection with several hundreds of frames per second on consumer hardware. In addition to introducing the core algorithm, we also introduce and discuss extensions for improving fitting precision. We validate the performance of all methods quantitatively on the 300W dataset, where we achieve state-of-the-art precision while requiring only a fraction of the runtime of most competing methods.

Cartilage functionality is determined by tissue structure and composition. If altered, cartilage is predisposed to premature degeneration. This pathomimetical study of early osteoarthritis evaluated the dose-dependant effects of collagenase-induced collagen disintegration and proteoglycan depletion on cartilage functionality as assessed by serial T1, T1ρ, T2, and T2* mapping under loading. 30 human femoral osteochondral samples underwent imaging on a clinical 3.0 T MRI scanner (Achieva, Philips) in the unloaded reference configuration (δ0) and under pressure-controlled quasi-static indentation loading to 15.1 N (δ1) and to 28.6 N (δ2). Imaging was performed before and after exposure to low (LC, 0.5 mg/mL; n = 10) or high concentration (HC, 1.5 mg/mL; n = 10) of collagenase. Untreated samples served as controls (n = 10). Loading responses were determined for the entire sample and the directly loaded (i.e. sub-pistonal) and bilaterally adjacent (i.e. peri‑pistonal) regions, referenced histologically, quantified as relative changes, and analysed using adequate parametric and non-parametric statistical tests. Dose-dependant surface disintegration and tissue loss were reflected by distinctly different pre- and post-exposure response-to-loading patterns. While T1 generally decreased with loading, regardless of collagenase exposure, T1ρ increased significantly after HC exposure (p = 0.008). Loading-induced decreases in T2 were significant after LC exposure (p = 0.006), while changes in T2* were ambiguous. In conclusion, aberrant loading-induced changes in T2 and T1ρ reflect moderate and severe matrix changes, respectively, and indicate the close interrelatedness of matrix changes and functionality in cartilage.

Magnetic resonance imaging (MRI) under mechanical loading, commonly referred to as stress MRI, allows the evaluation of functional properties of intra- and periarticular tissues non-invasively beyond static assessment. Quantitative MRI can identify physiological and pathological responses to loading as indication of, potentially treatable, early degeneration and load transmission failure. Therefore, we have developed and validated an MRI-compatible pressure-controlled axial loading device to compress human knee specimens under variable loading intensity and axis deviation. Ten structurally intact human knee specimens (mean age 83.2 years) were studied on a 3.0T scanner (Achieva, Philips). Proton density-weighted fat-saturated turbo spin-echo and high-resolution 3D water selective 3D gradient-echo MRI scans were acquired sequentially at 10° joint flexion in seven configurations: unloaded and then at approximately half and full body weight loading in neutral, 10° varus and 10° valgus alignment, respectively. Following manual segmentation in both femorotibial compartments, cartilage thickness (ThC) was determined as well as meniscus extrusion (ExM). These measures were compared to computed tomography scans, histological grading (Mankin and Pauli scores), and biomechanical properties (Instantaneous Young's Modulus). Compartmental, regional and subregional changes in ThC and ExM were reflective of loading intensity and joint alignment, with the greatest changes observed in the medial compartment during varus and in the lateral compartment during valgus loading. These were not significantly associated with the histological tissue status or biomechanical properties. In conclusion, this study explores the physiological in-situ response of knee cartilage and meniscus, based on stress MRI, and as a function of loading intensity, joint alignment, histological tissue status, and biomechanical properties, as another step towards clinical implementation.

Analysis of laboratory animal behavior allows assessment of animal wellbeing. We present a method for the classification of different activities of laboratory mice by analyzing video clips using three deep learning methods. Animals placed in observation cages are filmed and short video clips are labelled as belonging to one of five defined behaviors. Subsequently, three different methods based on convolutional neural networks (CNNS) are applied to classify the clips. The best performing method - a two-stream network that analyzes individual frames as well as the video’s optical flow - achieves an accuracy of 86.4%, including detection of important behavioral patterns such as self-grooming. These results show that the presented analysis protocol allows automated assessment of animal behavior by algorithmic analysis of videos of mice on observation boxes.

Next generation interferometers, such as the Square Kilometre Array, are set to obtain vast quantities of information about the kinematics of cold gas in galaxies. Given the volume of data produced by such facilities astronomers will need fast, reliable, tools to informatively filter and classify incoming data in real time. In this paper, we use machine learning techniques with a hydrodynamical simulation training set to predict the kinematic behaviour of cold gas in galaxies and test these models on both simulated and real interferometric data. Using the power of a convolutional autoencoder we embed kinematic features, unattainable by the human eye or standard tools, into a three-dimensional space and discriminate between disturbed and regularly rotating cold gas structures. Our simple binary classifier predicts the circularity of noiseless, simulated, galaxies with a recall of $85\%$ and performs as expected on observational CO and HI velocity maps, with a heuristic accuracy of $95\%$. The model output exhibits predictable behaviour when varying the level of noise added to the input data and we are able to explain the roles of all dimensions of our mapped space. Our models also allow fast predictions of input galaxies' position angles with a $1\sigma$ uncertainty range of $\pm17^{\circ}$ to $\pm23^{\circ}$ (for galaxies with inclinations of $82.5^{\circ}$ to $32.5^{\circ}$, respectively), which may be useful for initial parameterisation in kinematic modelling samplers. Machine learning models, such as the one outlined in this paper, may be adapted for SKA science usage in the near future., Comment: 15 pages, 11 figures

Biomechanical Magnetic Resonance Imaging (MRI) of articular cartilage, i.e. its imaging under loading, is a promising diagnostic tool to assess the tissue's functionality in health and disease. This study aimed to assess the response to static and dynamic loading of histologically intact cartilage samples by functional MRI and pressure-controlled in-situ loading. To this end, 47 cartilage samples were obtained from the medial femoral condyles of total knee arthroplasties (from 24 patients), prepared to standard thickness, and placed in a standard knee joint in a pressure-controlled whole knee-joint compressive loading device. Cartilage samples' responses to static (i.e. constant), dynamic (i.e. alternating), and no loading, i.e. free-swelling conditions, were assessed before (δ0), and after 30 min (δ1) and 60 min (δ2) of loading using serial T1ρ maps acquired on a 3.0T clinical MRI scanner (Achieva, Philips). Alongside texture features, relative changes in T1ρ (Δ1, Δ2) were determined for the upper and lower sample halves and the entire sample, analyzed using appropriate statistical tests, and referenced to histological (Mankin scoring) and biomechanical reference measures (tangent stiffness). Histological, biomechanical, and T1ρ sample characteristics at δ0 were relatively homogenous in all samples. In response to loading, relative increases in T1ρ were strong and significant after dynamic loading (Δ1 = 10.3 ± 17.0%, Δ2 = 21.6 ± 21.8%, p = 0.002), while relative increases in T1ρ after static loading and in controls were moderate and not significant. Generally, texture features did not demonstrate clear loading-related associations underlying the spatial relationships of T1ρ. When realizing the clinical translation, this in-situ study suggests that serial T1ρ mapping is best combined with dynamic loading to assess cartilage functionality in humans based on advanced MRI techniques.

Thermal infrared imaging is an emerging imaging modality allowing capturing heat radiation not detectable in the visible spectrum. In recent years, numerous applications of thermal infrared imaging for the processing of face images have been published. Many of these approaches only allow minimal head movement due to the lack of sufficiently robust face tracking and interpretation algorithms for thermal infrared images. To address this issue, we present a suite of interconnected algorithms for a number of typical facial image processing tasks face detection, head pose estimation, detection and tracking of facial landmarks and facial expression analysis. The modules can be used independently or in combination with each other. For combined use and as a demonstration of the versatility of our solution, we present a multiprocess solution based on a networking middleware that allows using all proposed algorithms to perform real-time face tracking and emotion recognition in thermal infrared images. The full code is made freely available on gitub under GNU license to allow incorporating our solutions into own projects.