Efficient and flexible sampling with blue noise properties of triangular meshes

Swin UNETR Segmentation with Automated Geometry Filtering for Biomechanical Modeling of Knee Joint Cartilage

PURPOSE

Simulation studies, such as finite element (FE) modeling, offer insights into knee joint biomechanics, which may not be achieved through experimental methods without direct involvement of patients. While generic FE models have been used to predict tissue biomechanics, they overlook variations in population-specific geometry, loading, and material properties. In contrast, subject-specific models account for these factors, delivering enhanced predictive precision but requiring significant effort and time for development.

METHODS

This study aimed to facilitate subject-specific knee joint FE modeling by integrating an automated cartilage segmentation algorithm using a 3D Swin UNETR. This algorithm provided initial segmentation of knee cartilage, followed by automated geometry filtering to refine surface roughness and continuity. In addition to the standard metrics of image segmentation performance, such as Dice similarity coefficient (DSC) and Hausdorff distance, the method's effectiveness was also assessed in FE simulation. Nine pairs of knee cartilage FE models, using manual and automated segmentation methods, were developed to compare the predicted stress and strain responses during gait.

RESULTS

The automated segmentation achieved high Dice similarity coefficients of 89.4% for femoral and 85.1% for tibial cartilage, with a Hausdorff distance of 2.3 mm between the automated and manual segmentation. Mechanical results including maximum principal stress and strain, fluid pressure, fibril strain, and contact area showed no significant differences between the manual and automated FE models.

CONCLUSION

These findings demonstrate the effectiveness of the proposed automated segmentation method in creating accurate knee joint FE models. The automated models developed in this study have been made publicly accessible to support biomechanical modeling and medical image segmentation studies ( https://data.mendeley.com/datasets/dc832g7j5m/1 ).

Blender tissue cartography: an intuitive tool for the analysis of dynamic 3D microscopy data

Tissue cartography extracts and cartographically projects surfaces from volumetric biological image data. This turns 3D- into 2D data which is much easier to visualize, analyze, and computationally process. Tissue cartography has proven particularly useful in developmental biology by taking advantage of the sheet-like organization of many biological tissues. However, existing software tools for tissue cartography are limited in the type of geometries they can handle and difficult for non-experts to use and extend. Here, we describe blender_tissue_cartography (btc), a tissue cartography add-on for the popular 3D creation software Blender. btc makes tissue cartography user-friendly via a graphical user interface and harnesses powerful algorithms from the computer graphics community for biological image analysis. The btc GUI enables interactive analysis and visualization without requiring any programming expertise, while an accompanying Python library allows expert users to create custom analysis pipelines. Both the add-on and the Python library are highly modular and fully documented, including interactive Jupyter Notebook tutorials. btc features a general-purpose pipeline for time-lapse data in which the user graphically defines a cartographic projection for a single key frame, which is propagated to all other frames via surface-to-surface alignment algorithms. The btc differential geometry module allows mathematically correcting for cartographic distortion, enabling faithful 3D measurements in 2D cartographic projections, including for vector fields like tissue flow fields. We demonstrate btc on diverse and complex tissue shapes from Drosophila, stem-cell-based organoids, Arabidopsis, and zebrafish.

Notochord segmentation in zebrafish controlled by iterative mechanical signaling

In bony fishes, patterning of the vertebral column, or spine, is guided by a metameric blueprint established in the notochord sheath. Notochord segmentation begins days after somitogenesis concludes and can occur in its absence. However, somite patterning defects lead to imprecise notochord segmentation, suggesting that these processes are linked. Here, we identify that interactions between the notochord and the axial musculature ensure precise spatiotemporal segmentation of the zebrafish spine. We demonstrate that myoseptum-notochord linkages drive notochord segment initiation by locally deforming the notochord extracellular matrix and recruiting focal adhesion machinery at these contact points. Irregular somite patterning alters this mechanical signaling, causing non-sequential and dysmorphic notochord segmentation, leading to altered spine development. Using a model that captures myoseptum-notochord interactions, we find that a fixed spatial interval is critical for driving sequential segment initiation. Thus, mechanical coupling of axial tissues facilitates spatiotemporal spine patterning.

Exploring reliable photogrammetry techniques for 3D modeling in anatomical research and education

In anatomical research and education, three-dimensional visualization of anatomical structures is crucial for understanding spatial relationships in diagnostics, surgical planning, and teaching. While computed tomography (CT) and magnetic resonance imaging (MRI) offer valuable insights, they are often expensive and require specialized resources. This study explores photogrammetry as an affordable and accessible approach for 3D modeling in anatomical contexts. Two photogrammetry methods were compared: conventional open-source software (Colmap) and Apple's RealityKit Object Capture. Human C3 vertebrae were imaged with a 24 MP camera, with and without a cross-polarization filter. Reconstruction times, vertex distances, surface area, and volume measurements were compared to CT scans. Results revealed that the Object Capture method surpassed the conventional approach in reconstruction speed and user-friendliness. Both methods exhibited similar vertex distance from reference mesh and volume measurements, although the conventional approach produced larger surface areas compared to CT-based models. Cross-polarization filters eliminated the need for pre-processing and improved outcomes in challenging lighting conditions. This study demonstrates that photogrammetry, especially Object Capture, as a reliable and time-efficient tool for 3D modeling in anatomical research and education. It offers accessible alternatives to traditional techniques with advantages in texture mapping. While further validation of various anatomical structures is required, the accessibility and cost-effectiveness of photogrammetry make it a valuable asset for the field. In summary, photogrammetry would have the potential to revolutionize anatomical research and education by providing cost-effective, accessible, and accurate 3D modeling. The study underscores the promise of advancing anatomical research and education through the integration of photogrammetry with ongoing improvements in user-friendliness and accessibility.

A computerized facial approximation method for Homo sapiens based on facial soft tissue thickness depths and geometric morphometrics

Facial approximation (FA) provides a promising means of generating the possible facial appearance of a deceased person. It facilitates exploration of the evolutionary forces driving anatomical changes in ancestral humans and can capture public attention. Despite the recent progress made toward improving the performance of FA methods, a limited understanding of detailed quantitative craniofacial relationships between facial bone and soft tissue morphology may hinder their accuracy, and hence subjective experience and artistic interpretation are required. In this study, we explored craniofacial relationships among human populations based upon average facial soft tissue thickness depths (FSTDs) and covariations between hard and soft tissues of the nose and mouth using geometric morphometrics. Furthermore, we proposed a computerized method to assign the learned craniofacial relationships to generate a probable facial appearance of Homo sapiens, reducing human intervention. A smaller resemblance comparison (an average Procrustes distance was 0.0258 and an average Euclidean distance was 1.79 mm) between approximated and actual faces and a greater recognition rate (91.67%) tested by a face pool indicated that average dense FSTDs contributed to raising the accuracy of approximated faces. Results of partial least squares (PLS) analysis showed that nasal and oral hard tissues have an effect on their soft tissues separately. However, relatively weaker RV correlations (<0.4) and greater approximation errors suggested that we need to be cautious about the accuracy of the approximated nose and mouth soft tissue shapes from bony structures. Overall, the proposed method can facilitate investigations of craniofacial relationships and potentially improve the reliability of the approximated faces for use in numerous applications in forensic science, archaeology, and anthropology.

Intrinsic and Isotropic Resampling for 3D Point Clouds

With rapid development of 3D scanning technology, 3D point cloud based research and applications are becoming more popular. However, major difficulties are still exist which affect the performance of point cloud utilization. Such difficulties include lack of local adjacency information, non-uniform point density, and control of point numbers. In this paper, we propose a two-step intrinsic and isotropic (I&I) resampling framework to address the challenge of these three major difficulties. The efficient intrinsic control provides geodesic measurement for a point cloud to improve local region detection and avoids redundant geodesic calculation. Then the geometrically-optimized resampling uses a geometric update process to optimize a point cloud into an isotropic or adaptively-isotropic one. The point cloud density can be adjusted to global uniform (isotropic) or local uniform with geometric feature keeping (being adaptively isotropic). The point cloud number can be controlled based on application requirement or user-specification. Experiments show that our point cloud resampling framework achieves outstanding performance in different applications: point cloud simplification, mesh reconstruction and shape registration. We provide the implementation codes of our resampling method at https://github.com/vvvwo/II-resampling.

Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment

Point cloud (PC)-a collection of discrete geometric samples of a 3D object's surface-is typically large, which entails expensive subsequent operations. Thus, PC sub-sampling is of practical importance. Previous model-based sub-sampling schemes are ad-hoc in design and do not preserve the overall shape sufficiently well, while previous data-driven schemes are trained for specific pre-determined input PC sizes and sub-sampling rates and thus do not generalize well. Leveraging advances in graph sampling, we propose a fast PC sub-sampling algorithm of linear time complexity that chooses a 3D point subset while minimizing a global reconstruction error. Specifically, to articulate a sampling objective, we first assume a super-resolution (SR) method based on feature graph Laplacian regularization (FGLR) that reconstructs the original high-res PC, given points chosen by a sampling matrix H. We prove that minimizing a worst-case SR reconstruction error is equivalent to maximizing the smallest eigenvalue λ_min of matrix H^T H+ μL, where L is a symmetric, positive semi-definite matrix derived from a neighborhood graph connecting the 3D points. To arrive at a fast algorithm, instead of maximizing λ_min, we maximize a lower bound λ^-_min(H^T H+ μL) via selection of H-this translates to a graph sampling problem for a signed graph G with self-loops specified by graph Laplacian L. We tackle this general graph sampling problem in three steps. First, we approximate G with a balanced graph G_B specified by Laplacian L_B. Second, leveraging a recent linear algebraic theorem called Gershgorin disc perfect alignment (GDPA), we perform a similarity transform L_p = SL_B S^-1, so that all Gershgorin disc left-ends of L_p are aligned exactly at λ_min(L_B). Finally, we choose samples on G_B using a previous graph sampling algorithm to maximize λ^-_min(H^T H+ μL_p) in linear time. Experimental results show that 3D points chosen by our algorithm outperformed competing schemes both numerically and visually in reconstruction quality.

Simulation of Mitral Valve Plasticity in Response to Myocardial Infarction

Left ventricular myocardial infarction (MI) has broad and debilitating effects on cardiac function. In many cases, MI leads to ischemic mitral regurgitation (IMR), a condition characterized by incompetency of the mitral valve (MV). IMR has many deleterious effects as well as a high mortality rate. While various clinical treatments for IMR exist, success of these procedures remains limited, in large part because IMR dramatically alters the geometry and function of the MV in ways that are currently not well understood. Previous investigations of post-MI MV remodeling have elucidated that MV tissues have a significant ability to undergo a form of permanent inelastic deformations in the first phase of the post-MI period. These changes appear to be attributable to the altered loading and boundary conditions on the MV itself, as opposed to an independent pathophysiological process. Mechanistically, these results suggest that the MV mostly responds passively to MI during the first 8 weeks post-MI by undergoing a permanent deformation. In the present study, we developed the first computational model of this post-MI MV remodeling process, which we term "mitral valve plasticity." Integrating methodologies and insights from previous studies of in vivo ovine MV function, image-based patient-specific model development, and post-MI MV adaptation, we constructed a representative geometric model of a pre-MI MV. We then performed finite element simulations of the entire MV apparatus under time-dependent boundary conditions and accounting for changes to material properties equivalent to those observed 0-8 weeks post-MI. Our results suggest that during this initial period of adaptation, the MV response to MI can be accurately modeled using a soft tissue plasticity approach, similar to permanent set frameworks that have been applied previously in the context of exogenously crosslinked tissues.

In vivo assessment of mitral valve leaflet remodelling following myocardial infarction

Each year, more than 40,000 people undergo mitral valve (MV) repair surgery domestically to treat regurgitation caused by myocardial infarction (MI). Although continual MV tissue remodelling following repair is believed to be a major contributor to regurgitation recurrence, the effects of the post-MI state on MV remodelling remain poorly understood. This lack of understanding limits our ability to predict the remodelling of the MV both post-MI and post-surgery to facilitate surgical planning. As a necessary first step, the present study was undertaken to noninvasively quantify the effects of MI on MV remodelling in terms of leaflet geometry and deformation. MI was induced in eight adult Dorset sheep, and real-time three-dimensional echocardiographic (rt-3DE) scans were collected pre-MI as well as at 0, 4, and 8 weeks post-MI. A previously validated image-based morphing pipeline was used to register corresponding open- and closed-state scans and extract local in-plane strains throughout the leaflet surface at systole. We determined that MI induced permanent changes in leaflet dimensions in the diastolic configuration, which increased with time to 4 weeks, then stabilised. MI substantially affected the systolic shape of the MV, and the range of stretch experienced by the MV leaflet at peak systole was substantially reduced when referred to the current time-point. Interestingly, when we referred the leaflet strains to the pre-MI configuration, the systolic strains remained very similar throughout the post-MI period. Overall, we observed that post-MI ventricular remodeling induced permanent changes in the MV leaflet shape. This predominantly affected the MV's diastolic configuration, leading in turn to a significant decrease in the range of stretch experienced by the leaflet when referenced to the current diastolic configuration. These findings are consistent with our previous work that demonstrated increased plastic (i.e. non-recoverable) leaflet deformations post-MI, that was completely accounted for by the associated changes in collagen fiber structure. Moreover, we demonstrated through noninvasive methods that the state of the MV leaflet can elucidate the progression and extent of MV adaptation following MI and is thus highly relevant to the design of current and novel patient specific minimally invasive surgical repair strategies.

A Local Graph-Based Structure for Processing Gigantic Aggregated 3D Point Clouds

We present an original workflow for structuring a point cloud generated from several scans. Our representation is based on a set of local graphs. Each graph is constructed from the depth map provided by each scan. The graphs are then connected together via the overlapping areas, and careful consideration of the redundant points in these regions leads to a piecewise and globally consistent structure for the underlying surface sampled by the point cloud. The proposed workflow allows structuring aggregated point clouds, scan after scan, whatever the number of acquisitions and the number of points per acquisition, even on computers with very limited memory capacities. To show that our structure can be highly relevant for the community, where the gigantic amount of data represents a real scientific challenge per se, we present an algorithm based on this structure capable of resampling billions of points on standard computers. This application is particularly attractive for simplifying and visualizing gigantic point clouds representing very large-scale scenes (buildings, urban scenes, historical sites...), which often require a prohibitive number of points to describe them accurately.

A DASKL Descriptor via Encoding the Information of Keypoints and a 3D Local Surface for 3D Matching

Three-dimensional matching is widely used in 3D vision tasks, such as 3D reconstruction, target recognition, and 3D model retrieval. The description of local features is the fundamental task of 3D matching; however, the descriptors only encode the surrounding surfaces of keypoints, and thus they cannot distinguish between similar local surfaces of objects. Therefore, we propose a novel local feature descriptor called deviation angle statistics of keypoints from local points and adjacent keypoints (DASKL). To encode a local surface fully, we first calculate a multiscale local reference axis (LRA); second, a local consistent strategy is used to redirect the normal direction, and the Poisson-disk sampling strategy is used to eliminate the redundancy in the data. Finally, the local surface is subdivided by two kinds of spatial features, and the histogram of the deviation angle between the LRA and the normal point in each subdivision space is generated. For the coding between keypoints, we calculate the LRA deviation angle between the nearest three keypoints and the adjacent keypoint. The performance of our DASKL descriptor is evaluated on several datasets (i.e., B3R, UWAOR, and LIDAR) with respect to Gaussian noise, varying mesh resolutions, clutter, and occlusion. The results show that our DASKL descriptor has achieved excellent performance in terms of description, robustness, and efficiency. Moreover, we further evaluate the generalization ability of the DASKL descriptor in a LIDAR real-scene dataset.

Cartilage thickness and bone shape variations as a function of sex, height, body mass, and age in young adult knees

The functional relationship between bone and cartilage is modulated by mechanical factors. Scarce data exist on the relationship between bone shape and the spatial distribution of cartilage thickness. The aim of the study was to characterise the coupled variation in knee bone morphology and cartilage thickness distributions in knees with healthy cartilage and investigate this relationship as a function of sex, height, body mass, and age. MR images of 51 knees from young adults (28.4 ± 4.1 years) were obtained from a previous study and used to train a statistical shape model of the femur, tibia, and patella and their cartilages. Five multiple linear regression models were fitted to characterise morphology as a function of sex, height, body mass, and age. A logistic regression classifier was fitted to characterise morphological differences between males and females, and tenfold cross-validation was performed to evaluate the models’ performance. Our results showed that cartilage thickness and its distribution were coupled to bone morphology. The first five shape modes captured over 90% of the variance and described coupled changes to the bone and spatial distribution of cartilage thickness. Mode 1 (size) was correlated to sex (p < 0.001) and height (p < 0.0001). Mode 2 (aspect ratio) was also correlated to sex (p = 0.006) and height (p = 0.017). Mode 4 (condylar depth) was correlated to sex only (p = 0.024). A logistic regression model trained on modes 1, 2, and 4 could classify sex with an accuracy of 92.2% (95% CI [81.1%, 97.8%]). No other modes were influenced by sex, height, body mass, or age. This study demonstrated the coupled relationship between bone and cartilage, showing that cartilage is thicker with increased bone size, diaphysis size, and decreased femoral skew. Our results show that sex and height influence bone shape and the spatial distribution of cartilage thickness in a healthy young adult population, but body mass and age do not.

SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction With Self-Projection Optimization

The task of point cloud upsampling aims to acquire dense and uniform point sets from sparse and irregular point sets. Although significant progress has been made with deep learning models, state-of-the-art methods require ground-truth dense point sets as the supervision, which makes them limited to be trained under synthetic paired training data and not suitable to be under real-scanned sparse data. However, it is expensive and tedious to obtain large numbers of paired sparse-dense point sets as supervision from real-scanned sparse data. To address this problem, we propose a self-supervised point cloud upsampling network, named SPU-Net, to capture the inherent upsampling patterns of points lying on the underlying object surface. Specifically, we propose a coarse-to-fine reconstruction framework, which contains two main components: point feature extraction and point feature expansion, respectively. In the point feature extraction, we integrate the self-attention module with the graph convolution network (GCN) to capture context information inside and among local regions simultaneously. In the point feature expansion, we introduce a hierarchically learnable folding strategy to generate upsampled point sets with learnable 2D grids. Moreover, to further optimize the noisy points in the generated point sets, we propose a novel self-projection optimization associated with uniform and reconstruction terms as a joint loss to facilitate the self-supervised point cloud upsampling. We conduct various experiments on both synthetic and real-scanned datasets, and the results demonstrate that we achieve comparable performances to state-of-the-art supervised methods.

Point Cloud Video Super-Resolution via Partial Point Coupling and Graph Smoothness

Point cloud (PC) is a collection of discrete geometric samples of a physical object in 3D space. A PC video consists of temporal frames evenly spaced in time, each containing a static PC at one time instant. PCs in adjacent frames typically do not have point-to-point (P2P) correspondence, and thus exploiting temporal redundancy for PC restoration across frames is difficult. In this paper, we focus on the super-resolution (SR) problem for PC video: increase point density of PCs in video frames while preserving salient geometric features consistently across time. We accomplish this with two ideas. First, we establish partial P2P coupling between PCs of adjacent frames by interpolating interior points in a low-resolution PC patch in frame t and translating them to a corresponding patch in frame t+1 , via a motion model computed by iterative closest point (ICP). Second, we promote piecewise smoothness in 3D geometry in each patch using feature graph Laplacian regularizer (FGLR) in an easily computable quadratic form. The two ideas translate to an unconstrained quadratic programming (QP) problem with a system of linear equations as solution-one where we ensure the numerical stability by upper-bounding the condition number of the coefficient matrix. Finally, to improve the accuracy of the ICP motion model, we re-sample points in a super-resolved patch at time t to better match a low-resolution patch at time t+1 via bipartite graph matching after each SR iteration. Experimental results show temporally consistent super-resolved PC videos generated by our scheme, outperforming SR competitors that optimized on a per-frame basis, in two established PC metrics.

VPU: A Video-Based Point Cloud Upsampling Framework

In this work, we propose a new patch-based framework called VPU for the video-based point cloud upsampling task by effectively exploiting temporal dependency among multiple consecutive point cloud frames, in which each frame consists of a set of unordered, sparse and irregular 3D points. Rather than adopting the sophisticated motion estimation strategy in video analysis, we propose a new spatio-temporal aggregation (STA) module to effectively extract, align and aggregate rich local geometric clues from consecutive frames at the feature level. By more reliably summarizing spatio-temporally consistent and complementary knowledge from multiple frames in the resultant local structural features, our method better infers the local geometry distributions at the current frame. In addition, our STA module can be readily incorporated with various existing single frame-based point upsampling methods (e.g., PU-Net, MPU, PU-GAN and PU-GCN). Comprehensive experiments on multiple point cloud sequence datasets demonstrate our video-based point cloud upsampling framework achieves substantial performance improvement over its single frame-based counterparts.

Custom Massive Allograft in a Case of Pelvic Bone Tumour: Simulation of Processing with Computerised Numerical Control vs. Robotic Machining

The use of massive bone allografts after the resection of bone tumours is still a challenging process. However, to overcome some issues related to the processing procedures and guarantee the best three-dimensional matching between donor and recipient, some tissue banks have developed a virtual tissue database based on the scanning of the available allografts for using their 3D shape during virtual surgical planning (VSP) procedures. To promote the use of future VSP bone-shaping protocols useful for machining applications within a cleanroom environment, in our work, we simulate a massive bone allograft machining with two different machines: a four-axes (computer numerical control, CNC) vs. a five-axes (robot) milling machine. The allograft design was based on a real case of allograft reconstruction after pelvic tumour resection and obtained with 3D Slicer and Rhinoceros software. Machining simulations were performed with RhinoCAM and graphically and mathematically analysed with CloudCompare and R, respectively. In this case, the geometrical differences of the allograft design are not clinically relevant; however, the mathematical analysis showed that the robot performed better than the four-axes machine. The proof-of-concept presented here paves the way towards massive bone allograft cleanroom machining. Nevertheless, further studies, such as the simulation of different types of allografts and real machining on massive bone allografts, are needed.

Marked and unmarked speed bump detection for autonomous vehicles using stereo vision

 A current challenge for autonomous vehicles is the detection of irregularities on road surfaces in order to prevent accidents; in particular, speed bump detection is an important task for safe and comfortable autonomous navigation. There are some techniques that have achieved acceptable speed bump detection under optimal road surface conditions, especially when signs are well-marked. However, in developing countries it is very common to find unmarked speed bumps and existing techniques fail. In this paper a methodology to detect both marked and unmarked speed bumps is proposed, for clearly painted speed bumps we apply local binary patterns technique to extract features from an image dataset. For unmarked speed bump detection, we apply stereo vision where point clouds obtained by the 3D reconstruction are converted to triangular meshes by applying Delaunay triangulation. A selection and extraction of the most relevant features is made to speed bump elevation on surfaces meshes. Results obtained have an important contribution and improve some of the existing techniques since the reconstruction of three-dimensional meshes provides relevant information for the detection of speed bumps by elevations on surfaces even though they are not marked.

Hybrid Variation-Aware Network for Angle-Closure Assessment in AS-OCT

Automatic angle-closure assessment in Anterior Segment OCT (AS-OCT) images is an important task for the screening and diagnosis of glaucoma, and the most recent computer-aided models focus on a binary classification of anterior chamber angles (ACA) in AS-OCT, i.e., open-angle and angle-closure. In order to assist clinicians who seek better to understand the development of the spectrum of glaucoma types, a more discriminating three-class classification scheme was suggested, i.e., the classification of ACA was expended to include open-, appositional- and synechial angles. However, appositional and synechial angles display similar appearances in an AS-OCT image, which makes classification models struggle to differentiate angle-closure subtypes based on static AS-OCT images. In order to tackle this issue, we propose a 2D-3D Hybrid Variation-aware Network (HV-Net) for open-appositional-synechial ACA classification from AS-OCT imagery. Specifically, taking into account clinical priors, we first reconstruct the 3D iris surface from an AS-OCT sequence, and obtain the geometrical characteristics necessary to provide global shape information. 2D AS-OCT slices and 3D iris representations are then fed into our HV-Net to extract cross-sectional appearance features and iris morphological features, respectively. To achieve similar results to those of dynamic gonioscopy examination, which is the current gold standard for diagnostic angle assessment, the paired AS-OCT images acquired in dark and light illumination conditions are used to obtain an accurate characterization of configurational changes in ACAs and iris shapes, using a Variation-aware Block. In addition, an annealing loss function was introduced to optimize our model, so as to encourage the sub-networks to map the inputs into the more conducive spaces to extract dark-to-light variation representations, while retaining the discriminative power of the learned features. The proposed model is evaluated across 1584 paired AS-OCT samples, and it has demonstrated its superiority in classifying open-, appositional- and synechial angles.

Fractional Super-Resolution of Voxelized Point Clouds

We present a method to super-resolve voxelized point clouds downsampled by a fractional factor, using lookup-tables (LUT) constructed from self-similarities from their own downsampled neighborhoods. The proposed method was developed to densify and to increase the precision of voxelized point clouds, and can be used, for example, as improve compression and rendering. We super-resolve the geometry, but we also interpolate texture by averaging colors from adjacent neighbors, for completeness. Our technique, as we understand, is the first specifically developed for intra-frame super-resolution of voxelized point clouds, for arbitrary resampling scale factors. We present extensive test results over different point clouds, showing the effectiveness of the proposed approach against baseline methods.

Enhanced head-skull shape learning using statistical modeling and topological features

Skull prediction from the head is a challenging issue toward a cost-effective therapeutic solution for facial disorders. This issue was initially studied in our previous work using full head-to-skull relationship learning. However, the head-skull thickness topology is locally shaped, especially in the face region. Thus, the objective of the present study was to enhance our head-to-skull prediction problem by using local topological features for training and predicting. Head and skull feature points were sampled on 329 head and skull models from computed tomography (CT) images. These feature points were classified into the back and facial topologies. Head-to-skull relations were trained using the partial least square regression (PLSR) models separately in the two topologies. A hyperparameter tuning process was also conducted for selecting optimal parameters for each training model. Thus, a new skull could be generated so that its shape was statistically fitted with the target head. Mean errors of the predicted skulls using the topology-based learning method were better than those using the non-topology-based learning method. After tenfold cross-validation, the mean error was enhanced 36.96% for the skull shapes and 14.17% for the skull models. Mean error in the facial skull region was especially improved with 4.98%. The mean errors were also improved 11.71% and 25.74% in the muscle attachment regions and the back skull regions respectively. Moreover, using the enhanced learning strategy, the errors (mean ± SD) for the best and worst prediction cases are from 1.1994 ± 1.1225 mm (median: 0.9036, coefficient of multiple determination (R²): 0.997274) to 3.6972 ± 2.4118 mm (median: 3.9089, R²: 0.999614) and from 2.0172 ± 2.0454 mm (median: 1.2999, R²: 0.995959) to 4.0227 ± 2.6098 mm (median: 3.9998, R²: 0.998577) for the predicted skull shapes and the predicted skull models respectively. This present study showed that more detailed information on the head-skull shape leads to a better accuracy level for the skull prediction from the head. In particular, local topological features on the back and face regions of interest should be considered toward a better learning strategy for the head-to-skull prediction problem. In perspective, this enhanced learning strategy was used to update our developed clinical decision support system for facial disorders. Furthermore, a new class of learning methods, called geometric deep learning will be studied.

Open source 3D phenotyping of chickpea plant architecture across plant development

BACKGROUND

Being able to accurately assess the 3D architecture of plant canopies can allow us to better estimate plant productivity and improve our understanding of underlying plant processes. This is especially true if we can monitor these traits across plant development. Photogrammetry techniques, such as structure from motion, have been shown to provide accurate 3D reconstructions of monocot crop species such as wheat and rice, yet there has been little success reconstructing crop species with smaller leaves and more complex branching architectures, such as chickpea.

RESULTS

In this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants. The imaging system we developed consists of a user programmable turntable and three cameras that automatically captures 120 images of each plant and offloads these to a computer for processing. The capture process takes 5-10 min for each plant and the majority of the reconstruction process on a Windows PC is automated. Plant height and total plant surface area were validated against "ground truth" measurements, producing R2 > 0.99 and a mean absolute percentage error < 10%. We demonstrate the ability to assess several important architectural traits, including canopy volume and projected area, and estimate relative growth rate in commercial chickpea cultivars and lines from local and international breeding collections. Detailed analysis of individual reconstructions also allowed us to investigate partitioning of plant surface area, and by proxy plant biomass.

CONCLUSIONS

Our results show that it is possible to use low-cost photogrammetry techniques to accurately reconstruct individual chickpea plants, a crop with a complex architecture consisting of many small leaves and a highly branching structure. We hope that our use of open-source software and low-cost hardware will encourage others to use this promising technique for more architecturally complex species.

A markerless platform for ambulatory systems neuroscience

Motor systems neuroscience seeks to understand how the brain controls movement. To minimize confounding variables, large-animal studies typically constrain body movement from areas not under observation, ensuring consistent, repeatable behaviors. Such studies have fueled decades of research, but they may be artificially limiting the richness of neural data observed, preventing generalization to more natural movements and settings. Neuroscience studies of unconstrained movement would capture a greater range of behavior and a more complete view of neuronal activity, but instrumenting an experimental rig suitable for large animals presents substantial engineering challenges. Here, we present a markerless, full-body motion tracking and synchronized wireless neural electrophysiology platform for large, ambulatory animals. Composed of four depth (RGB-D) cameras that provide a 360° view of a 4.5-square-meters enclosed area, this system is designed to record a diverse range of neuroethologically relevant behaviors. This platform also allows for the simultaneous acquisition of hundreds of wireless neural recording channels in multiple brain regions. As behavioral and neuronal data are generated at rates below 200 megabytes per second, a single desktop can facilitate hours of continuous recording. This setup is designed for systems neuroscience and neuroengineering research, where synchronized kinematic behavior and neural data are the foundation for investigation. By enabling the study of previously unexplored movement tasks, this system can generate insights into the functioning of the mammalian motor system and provide a platform to develop brain-machine interfaces for unconstrained applications.

Quantification of dental macrowear using 3D occlusal surface topographic measurements in deciduous and permanent molars of children

OBJECTIVES

Childhood paleodietary reconstruction via dental macrowear analysis is limited in part by available methods to measure dental macrowear. We describe a method to quantify dental macrowear progression (in both deciduous and permanent molars) using a handheld intraoral scanner and two 3D occlusal topographic measurements. We assess the agreement of our macrowear proxies with an established qualitative wear scoring system and their relationship to age.

MATERIAL AND METHODS

We scanned 92 well-preserved dentitions of immature individuals from the medieval cemetery of St. Pölten in Lower Austria using an intraoral scanner. Two measurements were made on the resulting mesh files-the relative flat surface area in % of the occlusal surface (RFSA%) and the mesial interior slope angle. We estimated the technical error of measurement (TEM). Comparisons were made with the macrowear scoring system-tooth wear index.

RESULTS

We found that TEM for both measurements was between 1 and 3%, except the interobserver TEM of RFSA% which was above 5%. Both quantitative measurements generally agree with the established qualitative scores and correlate with age; however, RFSA% does not reliably indicate the progression of macrowear for teeth after dentine exposure occurs.

DISCUSSION

The proposed 3D topographic measurements can be made reliably, and within a certain range of wear provide good quantitative proxies of the progression of dental macrowear. Such measurements constitute a promising approach for improving dental macrowear analysis in contexts such as childhood paleodietary reconstruction, which benefit from additional precision in wear rate estimation and present less dentine exposure.

Optimizing Mobility of Robotic Arms in Collision-free Motion Planning

A major task in motion planning is to find paths that have a high ability to react to external influences while ensuring a collision-free operation at any time. This flexibility is even more important in human-robot collaboration since unforeseen events can occur anytime. Such ability can be described as mobility, which is composed of two characteristics. First, the ability to manipulate, and second, the distance to joint limits. This mobility needs to be optimized while generating collision-free motions so that there is always the flexibility of the robot to evade dynamic obstacles in the future execution of generated paths. For this purpose, we present a Rapidly-exploring Random Tree (RRT), which applies additional costs and sampling methods to increase mobility. Additionally, we present two methods for the optimization of a generated path. Our first approach utilizes the built-in capabilities of the RRT*. The second method optimize the path with the stochastic trajectory optimization for motion planning (STOMP) approach with Gaussian Mixture Models. Moreover, we evaluate the algorithms in complex simulation and real environments and demonstrate an enhancement of mobility.

A hybrid, image-based and biomechanics-based registration approach to markerless intraoperative nodule localization during video-assisted thoracoscopic surgery

The resection of small, low-dense or deep lung nodules during video-assisted thoracoscopic surgery (VATS) is surgically challenging. Nodule localization methods in clinical practice typically rely on the preoperative placement of markers, which may lead to clinical complications. We propose a markerless lung nodule localization framework for VATS based on a hybrid method combining intraoperative cone-beam CT (CBCT) imaging, free-form deformation image registration, and a poroelastic lung model with allowance for air evacuation. The difficult problem of estimating intraoperative lung deformations is decomposed into two more tractable sub-problems: (i) estimating the deformation due the change of patient pose from preoperative CT (supine) to intraoperative CBCT (lateral decubitus); and (ii) estimating the pneumothorax deformation, i.e. a collapse of the lung within the thoracic cage. We were able to demonstrate the feasibility of our localization framework with a retrospective validation study on 5 VATS clinical cases. Average initial errors in the range of 22 to 38 mm were reduced to the range of 4 to 14 mm, corresponding to an error correction in the range of 63 to 85%. To our knowledge, this is the first markerless lung deformation compensation method dedicated to VATS and validated on actual clinical data.

Lightweight, compression-resistant cellular structures inspired from the infructescence of Liquidambar formosana

In order to adapt to the environment, plants have evolved many structural designs to improve material utilization. The head infructescence can be described as the Fibonacci sequence, in consistent with plant developmental biology. The lignified framework inside the head infructescence possesses idiographic structural designs that optimize maximum energy efficiency, growing space, seed spreading probability, and enhance the mechanical behavior of the infructescences. In this study, the hierarchical structure and mechanical properties of the infructescence of Liquidambar formosana, commonly called Formosan gum, were investigated. Liquidambar formosana has maple-like leaves and burr-like infructescences. The buckyball-like framework inside infructescence consists of chambers (cells), which support the whole structure under compression. Inspired by the framework, we proposed three models: Thomson model based on the lowest potential energy state, Poisson disc model indicated random distribution, and spherical Fibonacci model represented plant development. Three-dimensional physical entities of these models were fabricated by additive manufacturing. We discovered that under compression testing, these models appear different mechanical properties and deformation mechanisms based on their structures. Spherical Fibonacci model provides superior mechanical properties compared to Thomson and Poisson disc models due to its unique structural design. It is the first time that spherical Fibonacci model brought into the bio-inspired mechanics models through structural analysis and finite element method. The unique construction of Liquidambar formosana has great potential in the designs of novel lightweight, anti-buckling composites, and bio-inspired architectures.

A full-field 3D digital image correlation and modelling technique to characterise anterior cruciate ligament mechanics ex vivo

It is limiting to use conventional methods when characterising material properties of complex biological tissues with inhomogeneous and anisotropic structure, such as the anterior cruciate ligament (ACL) in the knee joint. This study aims to develop and utilise a three-dimensional digital image correlation method (3D DIC) for the purpose of determining material properties of femur-ACL-tibia complex across the surface without any contact between the tissue and the loading equipment. A full-field (360° view) 3D DIC test setup consisting of six digital single-lens reflex cameras was developed and ACL specimens from skeletally mature dog knee joints were tested. The six cameras were arranged into three pairs and the cameras within each pair were positioned with 25° in between to obtain the desired stereovision output. The test setup was calibrated twice: first to obtain the intrinsic and extrinsic parameters within camera pairs, and second to align the 3D surfaces from each camera pair in order to generate the full view of the ACLs. Using the undeformed 3D surfaces of the ligaments, ACL-specific finite element models were generated. Longitudinal deformation of ligaments under tensile loads obtained from the 3D DIC, and this was analysed to serve as input for the inverse finite element analysis. As a result, hyperelastic coefficients from the first-order Ogden model that characterise ACL behaviour were determined with a marginal error of <1.5%. This test setup and methodology provides a means to accurately determine inhomogeneous and anisotropic material properties of ACL. The methodology described in this study could be adopted to investigate other biological and cultured tissues with complex structure. STATEMENT OF SIGNIFICANCE: Determining the material properties of soft tissues with complex anatomical structure, such as the anterior cruciate ligament (ACL), is important to better understand their contribution to musculoskeletal biomechanics. Current conventional methods for characterising material properties of the ACL are often limited to a contact measurement approach, however an improved understanding of the mechanics of this complex tissue is vital in terms of preventing injury and developing novel therapies. This article reports the development and utilisation of non-contact optical methodology involving full-field three-dimensional digital image correlation and finite element analysis to accurately investigate material properties of the ACL, in a controlled environment. This technique reduces inaccuracies due to specimen clamping and more importantly considers the inhomogeneous nature of the examined tissue.

Laser-speckle-projection-based handheld anthropometric measurement system with synchronous redundancy reduction

Human body measurement is essential in modern rehabilitation medicine, which can be effectively combined with the technology of additive manufacturing. Digital image correlation based on laser speckle projection is a single-shot, accurate, and robust technique for human body measurement. In this paper, we present a handheld anthropometric measurement system based on laser speckle projection. A flexible retroreflective marker target is designed for multi-view data registration. Meanwhile, a synchronous redundancy-reduction algorithm based on a re-projected global disparity map is proposed. Experiment results validate that the proposed system is effective and accurate for different human body part measurements. Comparative experiments show that the proposed redundancy-reduction algorithm has high efficiency and can effectively preserve the features of complex shapes. The comprehensive performance of the algorithm is better than the other two tested methods.

3D Point Cloud Denoising Using Graph Laplacian Regularization of a Low Dimensional Manifold Model

3D point cloud-a new signal representation of volumetric objects-is a discrete collection of triples marking exterior object surface locations in 3D space. Conventional imperfect acquisition processes of 3D point cloud-e.g., stereo-matching from multiple viewpoint images or depth data acquired directly from active light sensors-imply non-negligible noise in the data. In this paper, we extend a previously proposed low-dimensional manifold model for the image patches to surface patches in the point cloud, and seek self-similar patches to denoise them simultaneously using the patch manifold prior. Due to discrete observations of the patches on the manifold, we approximate the manifold dimension computation defined in the continuous domain with a patch-based graph Laplacian regularizer, and propose a new discrete patch distance measure to quantify the similarity between two same-sized surface patches for graph construction that is robust to noise. We show that our graph Laplacian regularizer leads to speedy implementation and has desirable numerical stability properties given its natural graph spectral interpretation. Extensive simulation results show that our proposed denoising scheme outperforms state-of-the-art methods in objective metrics and better preserves visually salient structural features like edges.

Multisource Point Clouds, Point Simplification and Surface Reconstruction

As data acquisition technology continues to advance, the improvement and upgrade of the algorithms for surface reconstruction are required. In this paper, we utilized multiple terrestrial Light Detection And Ranging (Lidar) systems to acquire point clouds with different levels of complexity, namely dynamic and rigid targets for surface reconstruction. We propose a robust and effective method to obtain simplified and uniform resample points for surface reconstruction. The method was evaluated. A point reduction of up to 99.371% with a standard deviation of 0.2 cm was achieved. In addition, well-known surface reconstruction methods, i.e., Alpha shapes, Screened Poisson reconstruction (SPR), the Crust, and Algebraic point set surfaces (APSS Marching Cubes), were utilized for object reconstruction. We evaluated the benefits in exploiting simplified and uniform points, as well as different density points, for surface reconstruction. These reconstruction methods and their capacities in handling data imperfections were analyzed and discussed. The findings are that i) the capacity of surface reconstruction in dealing with diverse objects needs to be improved; ii) when the number of points reaches the level of millions (e.g., approximately five million points in our data), point simplification is necessary, as otherwise, the reconstruction methods might fail; iii) for some reconstruction methods, the number of input points is proportional to the number of output meshes; but a few methods are in the opposite; iv) all reconstruction methods are beneficial from the reduction of running time; and v) a balance between the geometric details and the level of smoothing is needed. Some methods produce detailed and accurate geometry, but their capacity to deal with data imperfection is poor, while some other methods exhibit the opposite characteristics.

3D-printable lung phantom for distal falloff verification of proton Bragg peak

In proton therapy, the Bragg peak of a proton beam reportedly deteriorates when passing though heterogeneous structures such as human lungs. Previous studies have used heterogeneous random voxel phantoms, in which soft tissues and air are randomly allotted to render the phantoms the same density as human lungs, for conducting Monte Carlo (MC) simulations. However, measurements of these phantoms are complicated owing to their difficult‐to‐manufacture shape. In the present study, we used Voronoi tessellation to design a phantom that can be manufactured, and prepared a Voronoi lung phantom for which both measurement and MC calculations are possible. Our aim was to evaluate the effectiveness of this phantom as a new lung phantom for investigating proton beam Bragg peak deterioration. For this purpose, we measured and calculated the percentage depth dose and the distal falloff widths (DFW) passing through the phantom. For the 155 MeV beam, the measured and calculated DFW values with the Voronoi lung phantom were 0.40 and 0.39 cm, respectively. For the 200 MeV beam, the measured and calculated DFW values with the Voronoi lung phantom were both 0.48 cm. Our results indicate that both the measurements and MC calculations exhibited high reproducibility with plastinated lung sample from human body in previous studies. We found that better results were obtained using the Voronoi lung phantom than using other previous phantoms. The designed phantom may contribute significantly to the improvement of measurement precision. This study suggests that the Voronoi lung phantom is useful for simulating the effects of the heterogeneous structure of lungs on proton beam deterioration.

Decal-Lenses: Interactive Lenses on Surfaces for Multivariate Visualization

We present decal-lenses, a new interaction technique that extends the concept of magic lenses to augment and manage multivariate visualizations on arbitrary surfaces. Our object-space lenses follow the surface geometry and allow the user to change the point of view during data exploration while maintaining a spatial reference to positions where one or more lenses were placed. Each lens delimits specific regions of the surface where one or more attributes can be selected or combined. Similar to 2D lenses, the user interacts with our lenses in real-time, switching between different attributes within the lens context. The user can also visualize the surface data representations from the point of view of each lens by using local cameras. To place lenses on surfaces of intricate geometry, such as the human brain, we introduce the concept of support surfaces for designing interaction techniques. Support surfaces provide a way to place and interact with the lenses while avoiding holes and occluded regions during data exploration. We further extend decal-lenses to arbitrary regions using brushing and lassoing operations. We discuss the applicability of our technique and present several examples where our lenses can be useful to create a customized exploration of multivariate data on surfaces.

Augmented Reality Mapping of Rock Mass Discontinuities and Rockfall Susceptibility Based on Unmanned Aerial Vehicle Photogrammetry

In rockfall hazard management, the investigation and detection of potential rockfall source areas on rock cliffs by remote-sensing-based susceptibility analysis are of primary importance. However, when the rockfall analysis results are used as feedback to the fieldwork, the irregular slope surface morphology makes it difficult to objectively locate the risk zones of hazard maps on the real slopes, and the problem of straightforward on-site visualization of rockfall susceptibility remains a research gap. This paper presents some of the pioneering studies on the augmented reality (AR) mapping of geospatial information from cyberspace within 2D screens to the physical world for on-site visualization, which directly recognizes the rock mass and superimposes corresponding rock discontinuities and rockfall susceptibility onto the real slopes. A novel method of edge-based tracking of the rock mass target for mobile AR is proposed, where the model edges extracted from unmanned aerial vehicle (UAV) structure-from-motion (SfM) 3D reconstructions are aligned with the corresponding actual rock mass to estimate the camera pose accurately. Specifically, the visually prominent edges of dominant structural planes were first explored and discovered to be a robust visual feature of rock mass for AR tracking. The novel approaches of visual-geometric synthetic image (VGSI) and prominent structural plane (Pro-SP) were developed to extract structural planes with identified prominent edges as 3D template models which could provide a pose estimation reference. An experiment verified that the proposed Pro-SP template model could effectively improve the edge tracking performance and quality, and this approach was relatively robust to the changes of sunlight conditions. A case study was carried out on a typical roadcut cliff in the Mentougou District of Beijing, China. The results validate the scalability of the proposed mobile AR strategy, which is applicable and suitable for cliff-scale fieldwork. The results also demonstrate the feasibility, efficiency, and significance of the geoinformation AR mapping methodology for on-site zoning and locating of potential rockfalls, and providing relevant guidance for subsequent detailed site investigation.

Bioinspired mechanical device generates plasma in water via cavitation

Nature can generate plasma in liquids more efficiently than human-designed devices using electricity, acoustics, or light. In the animal world, snapping shrimp can induce cavitation that collapses to produce high pressures and temperatures, leading to efficient plasma formation with photon and shock wave emission via energy focusing. Here, we report a bioinspired mechanical device that mimics the plasma generation technique of the snapping shrimp. This device was manufactured using additive manufacturing based on micro-x-ray computed tomography of a snapping shrimp claw molt. A spring fixture was designed to reliably actuate the claw with appropriate force and velocity to produce a high-speed water jet that matches the cavitation number and Reynolds number of the shrimp. Light emission and shocks were imaged, which indicate that our device reproduces the shrimp's plasma generation technique and is more efficient than other plasma generation methods.

A noninvasive method for the determination of in vivo mitral valve leaflet strains

Assessment of mitral valve (MV) function is important in many diagnostic, prognostic, and surgical planning applications for treatment of MV disease. Yet, to date, there are no accepted noninvasive methods for determination of MV leaflet deformation, which is a critical metric of MV function. In this study, we present a novel, completely noninvasive computational method to estimate MV leaflet in-plane strains from clinical-quality real-time three-dimensional echocardiography (rt-3DE) images. The images were first segmented to produce meshed medial-surface leaflet geometries of the open and closed states. To establish material point correspondence between the two states, an image-based morphing pipeline was implemented within a finite element (FE) modeling framework in which MV closure was simulated by pressurizing the open-state geometry, and local corrective loads were applied to enforce the actual MV closed shape. This resulted in a complete map of local systolic leaflet membrane strains, obtained from the final FE mesh configuration. To validate the method, we utilized an extant in vitro database of fiducially labeled MVs, imaged in conditions mimicking both the healthy and diseased states. Our method estimated local anisotropic in vivo strains with less than 10% error and proved to be robust to changes in boundary conditions similar to those observed in ischemic MV disease. Next, we applied our methodology to ovine MVs imaged in vivo with rt-3DE and compared our results to previously published findings of in vivo MV strains in the same type of animal as measured using surgically sutured fiducial marker arrays. In regions encompassed by fiducial markers, we found no significant differences in circumferential(P = 0.240) or radial (P = 0.808) strain estimates between the marker-based measurements and our novel noninvasive method. This method can thus be used for model validation as well as for studies of MV disease and repair.

Characterizing Anatomical Variability And Alzheimer's Disease Related Cortical Thinning in the Medial Temporal Lobe Using Graph-Based Groupwise Registration And Point Set Geodesic Shooting

The perirhinal cortex (PRC) is a site of early neurofibrillary tangle (NFT) pathology in Alzheimer's disease (AD). Subtle morphological changes in the PRC have been reported in MRI studies of early AD, which has significance for clinical trials targeting preclinical AD. However, the PRC exhibits considerable anatomical variability with multiple discrete variants described in the neuroanatomy literature. We hypothesize that different anatomical variants are associated with different patterns of AD-related effects in the PRC. Single-template approaches conventionally used for automated image-based brain morphometry are ill-equipped to test this hypothesis. This study uses graph-based groupwise registration and diffeomorphic landmark matching with geodesic shooting to build statistical shape models of discrete PRC variants and examine variant-specific effects of AD on PRC shape and thickness. Experimental results demonstrate that the statistical models recover the folding patterns of the known PRC variants and capture the expected shape variability within the population. By applying the proposed pipeline to a large dataset with subjects from different stages in the AD spectrum, we find 1) a pattern of cortical thinning consistent with the NFT pathology progression, 2) different patterns of the initial spatial distribution of cortical thinning between anatomical variants, and 3) an effect of AD on medial temporal lobe shape. As such, the proposed pipeline could have important utility in the early detection and monitoring of AD.

Novel Adaptive Laser Scanning Method for Point Clouds of Free-Form Objects

Laser scanners are widely used to collect coordinates, also known as point-clouds, of three-dimensional free-form objects. For creating a solid model from a given point-cloud and transferring the data from the model, features-based optimization of the point-cloud to minimize the number if points in the cloud is required. To solve this problem, existing methods mainly extract significant points based on local surface variation of a predefined level. However, comprehensively describing an object's geometric information using a predefined level is difficult since an object usually has multiple levels of details. Therefore, we propose a simplification method based on a multi-level strategy that adaptively determines the optimal level of points. For each level, significant points are extracted from the point cloud based on point importance measured by both local surface variation and the distribution of neighboring significant points. Furthermore, the degradation of perceptual quality for each level is evaluated by the adjusted mesh structural distortion measurement to select the optimal level. Experiments are performed to evaluate the effectiveness and applicability of the proposed method, demonstrating a reliable solution to optimize the adaptive laser scanning of point clouds for free-forms objects.

Ricci-flow based conformal mapping of the proximal femur to identify exercise loading effects

The causal relationship between habitual loading and adaptive response in bone morphology is commonly explored by analysing the spatial distribution of mechanically relevant features. In this study, 3D distribution of features in the proximal femur of 91 female athletes (5 exercise loading groups representing habitual loading) is contrasted with 20 controls. A femur specific Ricci-flow based conformal mapping procedure was developed for establishing correspondence among the periosteal surfaces. The procedure leverages the invariance of the conformal mapping method to isometric shape differences to align surfaces in the 2D parametric domain, to produce dense correspondences across an isotopological set of surfaces. This is implemented through a multi-parametrisation approach to detect surface features and to overcome the issue of inconsistency in the anatomical extent present in the data. Subsequently, the group-wise distribution of two mechanically relevant features was studied – cortical thickness and surface principal strains (simulation results of a sideways fall). Statistical inferences over the surfaces were made by contrasting the athlete groups with the controls through statistical parametric mapping. With the aid of group-wise and composite-group maps, proximal femur regions affected by specific loading groups were identified with a high degree of spatial localisation.

Rhorix: An interface between quantum chemical topology and the 3D graphics program blender

Chemical research is assisted by the creation of visual representations that map concepts (such as atoms and bonds) to 3D objects. These concepts are rooted in chemical theory that predates routine solution of the Schrödinger equation for systems of interesting size. The method of Quantum Chemical Topology (QCT) provides an alternative, parameter-free means to understand chemical phenomena directly from quantum mechanical principles. Representation of the topological elements of QCT has lagged behind the best tools available. Here, we describe a general abstraction (and corresponding file format) that permits the definition of mappings between topological objects and their 3D representations. Possible mappings are discussed and a canonical example is suggested, which has been implemented as a Python "Add-On" named Rhorix for the state-of-the-art 3D modeling program Blender. This allows chemists to use modern drawing tools and artists to access QCT data in a familiar context. A number of examples are discussed. © 2017 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Scene reassembly after multimodal digitization and pipeline evaluation using photorealistic rendering

Transparent objects require acquisition modalities that are very different from the ones used for objects with more diffuse reflectance properties. Digitizing a scene where objects must be acquired with different modalities requires scene reassembly after reconstruction of the object surfaces. This reassembly of a scene that was picked apart for scanning seems unexplored. We contribute with a multimodal digitization pipeline for scenes that require this step of reassembly. Our pipeline includes measurement of bidirectional reflectance distribution functions and high dynamic range imaging of the lighting environment. This enables pixelwise comparison of photographs of the real scene with renderings of the digital version of the scene. Such quantitative evaluation is useful for verifying acquired material appearance and reconstructed surface geometry, which is an important aspect of digital content creation. It is also useful for identifying and improving issues in the different steps of the pipeline. In this work, we use it to improve reconstruction, apply analysis by synthesis to estimate optical properties, and to develop our method for scene reassembly.

A Systematic Approach for Cross-Source Point Cloud Registration by Preserving Macro and Micro Structures

We propose a systematic approach for registering cross-source point clouds that come from different kinds of sensors. This task is especially challenging due to the presence of significant missing data, large variations in point density, scale difference, large proportion of noise, and outliers. The robustness of the method is attributed to the extraction of macro and micro structures. Macro structure is the overall structure that maintains similar geometric layout in cross-source point clouds. Micro structure is the element (e.g., local segment) being used to build the macro structure. We use graph to organize these structures and convert the registration into graph matching. With a novel proposed descriptor, we conduct the graph matching in a discriminative feature space. The graph matching problem is solved by an improved graph matching solution, which considers global geometrical constraints. Robust cross source registration results are obtained by incorporating graph matching outcome with RANSAC and ICP refinements. Compared with eight state-of-the-art registration algorithms, the proposed method invariably outperforms on Pisa Cathedral and other challenging cases. In order to compare quantitatively, we propose two challenging cross-source data sets and conduct comparative experiments on more than 27 cases, and the results show we obtain much better performance than other methods. The proposed method also shows high accuracy in same-source data sets.

How complexity increases in development: An analysis of the spatial-temporal dynamics of Gene expression in Ciona intestinalis

The increase in complexity in an embryo over developmental time is perhaps one of the most intuitive processes of animal development. It is also intuitive that the embryo becomes progressively compartmentalized over time and space. In spite of this intuitiveness, there are no systematic attempts to quantify how this occurs. Here, we present a quantitative analysis of the compartmentalization and spatial complexity of Ciona intestinalis over developmental time by analyzing thousands of gene expression spatial patterns from the ANISEED database. We measure compartmentalization in two ways: as the relative volume of expression of genes and as the disparity in gene expression between body parts. We also use a measure of the curvature of each gene expression pattern in 3D space. These measures show a similar increase over time, with the most dramatic change occurring from the 112-cell stage to the early tailbud stage. Combined, these measures point to a global pattern of increase in complexity in the Ciona embryo. Finally, we cluster the different regions of the embryo depending on their gene expression similarity, within and between stages. Results from this clustering analysis, which partially correspond to known fate maps, provide a global quantitative overview about differentiation and compartmentalization between body parts at each developmental stage.

Decal-Maps: Real-Time Layering of Decals on Surfaces for Multivariate Visualization

We introduce the use of decals for multivariate visualization design. Decals are visual representations that are used for communication; for example, a pattern, a text, a glyph, or a symbol, transferred from a 2D-image to a surface upon contact. By creating what we define as decal-maps, we can design a set of images or patterns that represent one or more data attributes. We place decals on the surface considering the data pertaining to the locations we choose. We propose a (texture mapping) local parametrization that allows placing decals on arbitrary surfaces interactively, even when dealing with a high number of decals. Moreover, we extend the concept of layering to allow the co-visualization of an increased number of attributes on arbitrary surfaces. By combining decal-maps, color-maps and a layered visualization, we aim to facilitate and encourage the creative process of designing multivariate visualizations. Finally, we demonstrate the general applicability of our technique by providing examples of its use in a variety of contexts.

Relative Scale Estimation and 3D Registration of Multi-Modal Geometry Using Growing Least Squares

The advent of low cost scanning devices and the improvement of multi-view stereo techniques have made the acquisition of 3D geometry ubiquitous. Data gathered from different devices, however, result in large variations in detail, scale, and coverage. Registration of such data is essential before visualizing, comparing and archiving them. However, state-of-the-art methods for geometry registration cannot be directly applied due to intrinsic differences between the models, e.g., sampling, scale, noise. In this paper we present a method for the automatic registration of multi-modal geometric data, i.e., acquired by devices with different properties (e.g., resolution, noise, data scaling). The method uses a descriptor based on Growing Least Squares, and is robust to noise, variation in sampling density, details, and enables scale-invariant matching. It allows not only the measurement of the similarity between the geometry surrounding two points, but also the estimation of their relative scale. As it is computed locally, it can be used to analyze large point clouds composed of millions of points. We implemented our approach in two registration procedures (assisted and automatic) and applied them successfully on a number of synthetic and real cases. We show that using our method, multi-modal models can be automatically registered, regardless of their differences in noise, detail, scale, and unknown relative coverage.

Toner Savings Based on Quasi-Random Sequences and a Perceptual Study for Green Printing

Toner savings in monochromatic printing are an important target for improving green computing performance and more specifically green printing. In order to extend the lifetime of the printer cartridge, some options are available for laser printers, usually reducing the number of dots with respect to the normal print quality. However available algorithms and patents do not provide a method for dynamically adapting the percentage of toner savings to the required printing quality. In this paper, we introduce a new quasi-random sequence-based algorithm for reducing the number of dots in the printing process, able to achieve optimal discrepancy and low computational complexity, for all print quality levels. In order to reduce patterns in the removed dots, blue noise dithering is applied when the desired percentage of toner savings is moderate. The proposed solution can be easily implemented in the printer firmware, given its low computational complexity. In order to verify the results from a perceptual point of view, an extended test with 135 volunteers and more than 5000 comparisons has been performed, besides checking that toner is effectively saved. Results show that the proposed approach can produce a reduction of the perceived quality almost directly proportional to the number of monochromatic dots skipped, with only a reduced influence from the font used. The perceptual results are better in the proposal than in the previous approaches. The proposed algorithm appears to be a promising technique for improving green printing in monochromatic laser printers without using custom fonts.