Local Shape Variations Research Articles

Liver shape analysis using statistical parametric maps at population scale

BackgroundMorphometric image analysis enables the quantification of differences in the shape and size of organs between individuals.MethodsHere we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three dimensional mesh vertices, we evaluated local shape variations and modelled their association with anthropometric, phenotypic and clinical conditions, including liver disease and type-2 diabetes.ResultsWe found that age, body mass index, hepatic fat and iron content, as well as, health traits were significantly associated with regional liver shape and size. Interaction models in groups with specific clinical conditions showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variations in both type-2 diabetes and liver disease.ConclusionsThe results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions.

Dense Depth-Guided Generalizable NeRF

Neural rendering approaches enable photo-realistic rendering on novel view synthesis tasks while their per-scene optimization remains an issue for scalability. Recent methods introduce novel neural radiance field (NeRF) frameworks that generalize to unseen scenes on-the-fly by combining multi-view stereo with differentiable volume rendering. These generalizable NeRF methods synthesize the colors of 3D ray points by learning the consistency of image features projected from given nearby views. Since the consistency is computed on the 2D projected image space, it is vulnerable to occlusion and local shape variation by viewing direction. To solve this problem, we present dense depth-guided generalizable NeRF that leverages the depth as the signed distance between the ray point and the object surface of the scene. We first generate the dense depth maps from sparse 3D points of structure from motion (SfM) which is an inevitable step to obtain camera poses. Next, the dense depth maps are exploited as complementary features invariant to the sparsity of nearby views and mask for occlusion handling. Experiments demonstrate that our approach outperforms existing generalizable NeRF methods for widely used real and synthetic datasets.

Environmental signal in the evolutionary diversification of bird skeletons.

Characterizing how variation in the tempo and mode of evolution has structured the phenotypic diversity of extant species is a central goal of macroevolution1-3. However, studies are typically limited to a handful of traits4-6, providing incomplete information. We analyse morphological diversification in living birds, an ecologically diverse group7, documenting structural scales from 'pan-skeletal' proportions down to the localized three-dimensional shape changes of individual bones. We find substantial variation in evolutionary modes among avian subgroups and among skeletal parts, indicating widespread mosaicism and possible differences in the structure of the macroevolutionary landscape across Earth's main environments. Water-linked groups, especially Aequorlitornithes (waterbirds), have repeatedly explored a large portion of their total morphospace, emphasizing variation in body proportions and in the shape of bones close to the body core, which are functionally related to the mechanics of locomotion8. By contrast, landbirds (Inopinaves) evolved distinct, group-specific body forms early in the aftermath of the K-Pg and subsequently emphasized local shape variation, especially in the head and distal limb bones, which interact more directly with the environment. Passerines, which comprise more than half of all bird species, show a conservative evolutionary dynamic that resulted in low disparity across all skeletal parts. Evidence for early establishment of the morphospace of living birds is clear for some skeletal parts, including beaks and the combined skeletal morphology. However, we find little evidence for early partitioning of that morphospace, contrary to more specific predictions of 'niche-filling' models1,9. Nevertheless, early divergence among broad environmental types may have caused an early divergence of evolutionary modes, suggesting an important role for environmental divergence in structuring the radiation of crown-group birds.

The perceived value of human-AI collaboration in early shape exploration: An exploratory assessment.

As a vital element of early shape exploration, divergence can be time-consuming and challenging, with iterative cycles where idea fixation and creative blocks must be overcome for fuzzy ideas to be fully expanded and understood. Despite interesting tools that have been developed for this purpose, some important challenges remain, as it appears that many designers still prefer simple freehand sketching and tend to defer the use of computational tools to later stages. This work presents an exploratory assessment of the perceived value of a new tool, Shapi, developed to assist early shape exploration by addressing some of the pitfalls reported in the literature. Shapi is envisioned as an autonomous assistant that provides local and global shape variations in the form of rough sketches based on an initial human sketch and interactive cycles. These shape variations are What-If scenarios and cognitive facilitators that may spark new ideas or enable a deeper understanding of the shape and the identification of interesting patterns. Shapi’s capabilities are explored in a diverse set of case studies with different purposes: nine implementations in industrial design, three in graphic design, and five with open-ended artistic purposes. These implementations are then used in a survey about initial perceived value in which the majority gave high ratings in terms of exploration (75.5% ≥ 4 out of 5), interpretation (83.7% ≥ 4), adaptation (77.6% ≥ 4), value (73.5% ≥ 4), creativity (69.4% ≥ 4), and general interest in the tool (79.6% ≥ 4). This work brings insight into promising functionalities, opportunities, and risks in the intersection between artificial intelligence, design, and art.

Self-tuned ultrasonic elliptical vibration cutting for high-efficient machining of micro-optics arrays on brittle materials

Ultrasonic elliptical vibration cutting is a very promising technique for the machining of brittle materials. However, its machining performance is currently limited by the ductile machining model and the machining strategy with a constant feed rate, leading to low machining efficiency. To overcome this defect, this paper presents a novel self-tuned ultrasonic elliptical vibration cutting (SUEVC) technique to achieve high-efficient ductile-regime machining of the micro-optics array on brittle materials. The proposed SUEVC includes a ductile-regime machining model and a tool path generation method. In SUEVC, the feed rate adaptively changes with respect to the local shape variation of the desired surface along the feeding direction to ensure both crack-free surface and high machining efficiency. Finally, two 1 × 3 spherical micro-optics arrays were successfully fabricated on single-crystal MgF2 by SUEVC and the traditional machining strategy respectively. Results demonstrated that the SUEVC could enhance the machining efficiency by 30% relative to the traditional machining strategy, while maintaining similar surface roughness and a crack-free surface.

Statistical shape model-based planning organ-at-risk volume: application to pancreatic cancer patients

Purpose. To introduce the concept of statistical shape model (SSM)-based planning organ-at-risk volume (sPRV) for pancreatic cancer patients. Methods. A total of 120 pancreatic cancer patients were enrolled in this study. After correcting inter-patient variations in the centroid position of the planning target volume (PTV), four different SSMs were constructed by registering a deformable template model to an individual model for the stomach and duodenum. The sPRV, which focused on the following different components of the inter-patient variations, was then created: Scenario A: shape, rotational angle, volume, and centroid position; Scenario B: shape, rotational angle, and volume; Scenario C: shape and rotational angle; and Scenario D: shape. The conventional PRV (cPRV) was created by adding an isotropic margin R (3–15 mm) to the mean shape model. The corresponding sPRV was created from the SSM until the volume difference between the cPRV and sPRV was less than 1%. Thereafter, we computed the overlapping volume between the PTV and cPRV (OLc) or sPRV (OLs) in each patient. OLs being larger than OLc implies that the local shape variations in the corresponding OAR close to the PTV were large. Therefore, OLs/OLc was calculated in each patient for each R-value, and the median value of OLs/OLc was regarded as a surrogate for plan quality for each R-value. Results. For R = 3 and 5 mm, OLs/OLc exceeded 1 for the stomach and duodenum in all scenarios, with a maximum OLs/OLc of 1.21. This indicates that smaller isotropic margins did not sufficiently account for the local shape changes close to the PTV. Conclusions. Our results indicated that, in contrast to conventional PRV, SSM-based PRVs, which account for local shape changes, would result in better dose sparing for the stomach and duodenum in pancreatic cancer patients.

The Influence of Ecosystem and Phylogeny on Tropical Tree Crown Size and Shape

The sizes and shapes of tree crowns are of fundamental importance in ecology, yet understanding the forces that determine them remains elusive. A cardinal question facing ecologists is the degree to which general and non-specific versus ecological and context-dependent processes are responsible for shaping tree crowns. Here, we test this question for the first time across diverse tropical ecosystems. Using trees from 20 plots varying in elevation, precipitation, and ecosystem type (savanna-forest transitions) across the paleo- and neo-tropics, we test the relationship between crown dimensions and tree size. By analyzing these scaling relationships across environmental gradients, biogeographic regions, and phylogenetic distance, we extend Metabolic Scaling Theory (MST) predictions to include how local selective pressures shape variation in crown dimensions. Across all sites, we find strong agreement between mean trends and MST predictions for the scaling of crown size and shape, but large variation around the mean. While MST explained approximately half of the observed variation in tree crown dimensions, we find that local, ecosystem, and phylogenetic predictors account for the half of the residual variation. Crown scaling does not change significantly across regions, but does change across ecosystem types, where savanna tree crowns grow more quickly with tree girth than forest tree crowns. Crowns of legumes were wider and more voluminous than those of other taxa. Thus, while MST can accurately describe the central tendency of tree crown size, local ecological conditions and evolutionary history appear to modify the scaling of crown shape. Importantly, our extension of MST incorporating these differences accounts for the mechanisms driving variation in the scaling of crown dimensions across the tropics. We present allometric equations for the prediction of crown dimensions across tropical ecosystems. These results are critical when scaling the function of individual trees to larger spatial scales or incorporating the size and shape of tree crowns in global biogeochemical models.

Parametric hull shape variations by Reduced Order Model based geometric transformation

The representation and the variation of hull shapes are two challenging problems in naval architecture due to the complexity of the geometry and to the need to ensure the fairness of the surfaces. Conventional CAD techniques are widely used to accurately describe the hull shape. However, they are rather complex to be easily used to generate hull shape variations due to the great number of variables involved. We propose a study to highlight the pro and cons of the application of space reduction techniques, usually referred to as Reduced Order Models (ROMs), to create a parametric model for both global and local hull shape variations. A geometric transformation relying on the Proper Orthogonal Decomposition (POD) method is applied on top of a combined subdivision surface — Free Form Deformation (FFD) approach conceived for modeling and variation of hull shapes. The analysis focuses on highlighting the geometric meaning of the new POD basis functions and on how this model affects the variability of the explored design space. Two studies are developed in order to show possible applications of this technique. In the first one a new set of selection criteria for the POD modes based on geometric considerations are proposed to use it on uncorrelated geometric domains. In the latter the large-dimensional space of the so-called Design Velocities (DV), related to the shape sensitivity, is reduced by using the POD approach to create a new transformation of the hull shape.

Morphometry and spatial distribution of glacial cirques in the Western Fuegian Andes of Argentina, southernmost South America

Glacial cirques comprise a distinctive element of relief in alpine landscapes, and their morphometry, distribution and spatial arrangement are generally linked to morphoclimatic factors. This study provides an analysis of morphometric characteristics, floor altitude, aspect and distribution of 251 first-order and 99 "inner" cirques with regard to environmental agents in a selected alpine-type area of the southwestern Fuegian Andes, in Southern Argentina. Parametric variables were extracted and quantified from satellite images and a Digital Elevation Model, and integrated to a Geographical Information System for their systematization and processing. Significant associations were identified between the different geometric and spatial parameters evaluated. Variability in cirque morphometry along the study area suggests that their evolution was regulated to a large degree by glacial dynamics related to Beagle palaeoglacier activity, and by the W-E topographic gradient of the mountain section. The record indicates that most cirques in the Fuegian Andes have undergone allometric enlargement in the three dimensions (i.e., lesser vertical erosion compared to walls' horizontal expansion), with local variations in size and shape controlled by ice residence time, aspect and altitude, whereas geological structure is supposed to have affected cirque formation along the entire mountain chain. Cirque azimuth potentially responds to the combined influence of climate (solar radiation and westerly winds), structure (pervasive faulting and rock bedding) and topography in their development. In particular, dominance of southeasterly aspects is interpreted as a consequence of a relatively cloud-free atmosphere and aridity during past glacial periods in the region, consistently with published evidence. The absence of aspect-related variation in cirque floor altitude is considered to indicate incidence of extensive glaciations in the area (i.e., mostly ice-sheet conditions, with mountain peaks far above the regional equilibrium line altitude or ELA), as well as formation of younger, lower cirques during successive cold periods of different intensity. Occurrence of "inner" cirques inside larger (preexisting) cirques is thought to reflect a temporary positioning of the ELA just above the floor of the container cirque, permitting short-lived, small cirque-type glaciers to develop along structural bedrock weaknesses.

Cover Image

COVER ILLUSTRATION Morphological findings from Jenkins et al. (this issue) Subcortical structural variations associated with low socioeconomic status in adolescents With art by AD_Images and aitoff, Pixabay. Figure depicts Left lateral view of results from subcortical vertex-wise linear regression analysis of association between Income:Poverty ratio in females aged 14 years, showing a mostly negative relationship between socioeconomic status and local shape variation.

Subcortical structural variations associated with low socioeconomic status in adolescents

Low socioeconomic status (SES) is associated with a higher probability of multiple exposures (e.g., neighborhood violence, poor nutrition, housing instability, air pollution, and insensitive caregiving) known to affect structural development of subcortical brain regions that subserve threat and reward processing, however, few studies have examined the relationship between SES and such subcortical structures in adolescents. We examined SES variations in volume and surface morphometry of subcortical regions. The sample comprised 256 youth in eighth grade (mean age = 13.9 years), in whom high dimensional deformation mapping of structural 3T magnetic resonance imaging scans was performed. Vertex‐wise linear regression analyses examined associations between income to poverty ratio and surfaces of the hippocampus, amygdala, thalamus, caudate, putamen, nucleus accumbens and pallidum, with the covariates age, pubertal status, and intracranial volume. Given sex differences in pubertal development and subcortical maturation at this age, the analyses were stratified by sex. Among males, who at this age average an earlier pubertal stage than females, the relationship between SES and local shape variation in subcortical regions was almost entirely positive. For females, the relationship between SES and local shape variation was negative. Racial identity was associated with SES in our sample, however supplementary analyses indicated that most of the associations between SES and subcortical structure were independent of it. Although these cross‐sectional results are not definitive, they are consistent with a scenario where low SES delays structural maturation of subcortical regions involved with threat and reward processing. Future longitudinal studies are needed to test this hypothesis.

Experimental characterization of the spatiotemporal dynamics of a turbulent flame in a gas turbine model combustor using computed tomography of chemiluminescence

An experimental study was conducted based on the recently available computed tomography of chemiluminescence (CTC) technique with a swirl-stabilized gas turbine model combustor to obtain both time-resolved and three-dimensional (3D) dynamics of the turbulent flame in confined situation. The CTC system employed in the present study was designed in a cost-effective, but highly-efficient manner, which allows simultaneous recordings of six projections of the target flame with three cameras at a repetition rate of 1 kHz. Instantaneous flame topologies were successfully captured and highly-transient flame dynamics in the combustion chamber, such as local extinction and flame shape variations were also observed. A further analysis based on the instantaneous and phase-averaged 3D movements of the flame centroids suggested that the flame in the present combustor exhibited a helical but relatively stochastic propagation and a distinct azimuthal rotation around the combustor, which is in the direction of rotation imposed by the swirler to the flow. Results obtained in this work demonstrate the capability of the CTC technique to resolve the detailed spatiotemporal dynamics of turbulent flames in confined combustion systems, which is expected to be particularly helpful for the study of combustion instabilities, such as periodic combustion oscillations in fuel-lean gas turbine engines.

Morphometric Variation of the Aedes albifasciatus (Diptera: Culicidae) Wings in Three Populations From Different Ecoregions of Argentina

Shape variability among individuals is important to understand some ecological relationships, since it provides the nexus between the genotype and the environment. Geometric morphometrics based on generalized procrustes analysis was applied on 17 landmarks of the wings of Aedes albifasciatus (Macquart 1838) (Diptera: Culicidae) females collected from three ecoregions of Argentina (Delta and islands of the Paraná River, Pampa, and Patagonian steppe). This methodology was used to discriminate the shapes of individuals belonging to different regions. The population of the Patagonian steppe, which was the most geographically distant, showed the most dissimilar shape. Different local variations in wing shape could have been selected according to the environmental characteristics and maintained by geographic isolation. The individuals of the two ecoregions closest to each other (Delta and islands of the Paraná River and Pampa) showed differences in shape that can be explained by a lower gene flow due to the effect of geographic isolation (by the Paraná River) and the limited dispersive capacity of Ae. albifasciatus. The results allow concluding that both environmental diversity and geographic barriers could contribute to local variations in wing shape.

A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images.

Heart motion tracking for radiation therapy treatment planning can result in effective motion management strategies to minimize radiation-induced cardiotoxicity. However, automatic heart motion tracking is challenging due to factors that include the complex spatial relationship between the heart and its neighboring structures, dynamic changes in heart shape, and limited image contrast, resolution, and volume coverage. In this study, we developed and evaluated a deep generative shape model-driven level set method to address these challenges. The proposed heart motion tracking method makes use of a heart shape model that characterizes the statistical variations in heart shapes present in a training data set. This heart shape model was established by training a three-layered deep Boltzmann machine (DBM) in order to characterize both local and global heart shape variations. During the tracking phase, a distance regularized level-set evolution (DRLSE) method was applied to delineate the heart contour on each frame of a cine MRI image sequence. The trained shape model was embedded into the DRLSE method as a shape prior term to constrain an evolutional shape to reach the desired heart boundary. Frame-by-frame heart motion tracking was achieved by iteratively mapping the obtained heart contour for each frame to the next frame as a reliable initialization, and performing a level-set evolution. The performance of the proposed motion tracking method was demonstrated using thirty-eight coronal cine MRI image sequences.

Texture and Geometry Scattering Representation-Based Facial Expression Recognition in 2D+3D Videos

Facial Expression Recognition (FER) is one of the most important topics in the domain of computer vision and pattern recognition, and it has attracted increasing attention for its scientific challenges and application potentials. In this article, we propose a novel and effective approach to FER using multi-model two-dimensional (2D) and 3D videos, which encodes both static and dynamic clues by scattering convolution network. First, a shape-based detection method is introduced to locate the start and the end of an expression in videos; segment its onset, apex, and offset states; and sample the important frames for emotion analysis. Second, the frames in Apex of 2D videos are represented by scattering, conveying static texture details. Those of 3D videos are processed in a similar way, but to highlight static shape details, several geometric maps in terms of multiple order differential quantities, i.e., Normal Maps and Shape Index Maps, are generated as the input of scattering, instead of original smooth facial surfaces. Third, the average of neighboring samples centred at each key texture frame or shape map in Onset is computed, and the scattering features extracted from all the average samples of 2D and 3D videos are then concatenated to capture dynamic texture and shape cues, respectively. Finally, Multiple Kernel Learning is adopted to combine the features in the 2D and 3D modalities and compute similarities to predict the expression label. Thanks to the scattering descriptor, the proposed approach not only encodes distinct local texture and shape variations of different expressions as by several milestone operators, such as SIFT, HOG, and so on, but also captures subtle information hidden in high frequencies in both channels, which is quite crucial to better distinguish expressions that are easily confused. The validation is conducted on the BU-4DFE and BP-4D databa ses, and the accuracies reached are very competitive, indicating its competency for this issue.

Recognition of Text-based CAPTCHA with Merged Characters

This paper intends to introduce an approach based on generalized Hough transform with the aim to recognize the CAPTCHA with merged characters. Firstly, pre-processing and skeletonization are applied to the CAPTCHA. In the meantime, the local features of the edge points in template image are extracted and saved in the reference table. During the GHT voting phase, each object pixel of the CAPTCHA skeletonization image is compared with the reference table to accumulate the voting array. Therefore, the coordinates of the potential reference points are located by finding the maxima of the array. Finally, the interference of neighboring characters is analyzed to heighten the recognition rate. Due to the shift and rotational invariance of the local features, this method can effectively solve the recognition of slightly merged CAPTCHA with local shape variation and rotational characters.

Sparse Geometric Representation Through Local Shape Probing.

We propose a new shape analysis approach based on the non-local analysis of local shape variations. Our method relies on a novel description of shape variations, called Local Probing Field (LPF), which describes how a local probing operator transforms a pattern onto the shape. By carefully optimizing the position and orientation of each descriptor, we are able to capture shape similarities and gather them into a geometrically relevant dictionary over which the shape decomposes sparsely. This new representation permits to handle shapes with mixed intrinsic dimensionality (e.g., shapes containing both surfaces and curves) and to encode various shape features such as boundaries. Our shape representation has several potential applications; here we demonstrate its efficiency for shape resampling and point set denoising for both synthetic and real data.

Multi-resolution multi-object statistical shape models based on the locality assumption.

Statistical shape models learned from a population of previously observed training shapes are nowadays widely used in medical image analysis to aid segmentation or classification. However, providing an appropriate and representative training population of preferably manual segmentations is typically either very labor-intensive or even impossible. Therefore, statistical shape models in practice frequently suffer from the high-dimension-low-sample-size (HDLSS) problem resulting in models with insufficient expressiveness. In this paper, a novel approach for learning representative multi-resolution multi-object statistical shape models from a small number of training samples that adequately model the variability of each individual object as well as their interrelations is presented. The method is based on the assumption of locality, which means that local shape variations have limited effects in distant areas and, therefore, can be modeled independently. This locality assumption is integrated into the standard statistical shape modeling framework by manipulating the sample covariance matrix (non-zero covariances between distant landmarks are set to zero). To allow for multi-object modeling, a method for computing distances between points located on different object shapes is proposed. Furthermore, different levels of locality are introduced by deriving a multi-resolution scheme, which is equipped with a method to combine variability information modeled at different levels into a single shape model. This combined representation of global and local variability in a single shape model allows the use of the classical active shape model strategy for model-based image segmentation. An extensive evaluation based on a public data base of 247 chest radiographs is performed to show the modeling and segmentation capabilities of the proposed approach in single- and multi-object HDLSS scenarios. The new approach is not only compared to the classical shape modeling method but also to three state-of-the-art shape modeling approaches specifically designed to cope with the HDLSS problem. The results show that the new approach significantly outperforms all other approaches in terms of generalization ability and model-based segmentation accuracy.

Single shot fringe pattern phase demodulation using Hilbert-Huang transform aided by the principal component analysis.

Hybrid single shot algorithm for accurate phase demodulation of complex fringe patterns is proposed. It employs empirical mode decomposition based adaptive fringe pattern enhancement (i.e., denoising, background removal and amplitude normalization) and subsequent boosted phase demodulation using 2D Hilbert spiral transform aided by the Principal Component Analysis method for novel, correct and accurate local fringe direction map calculation. Robustness to fringe pattern significant noise, uneven background and amplitude modulation as well as local fringe period and shape variations is corroborated by numerical simulations and experiments. Proposed automatic, adaptive, fast and comprehensive fringe analysis solution compares favorably with other previously reported techniques.

Adaptive Diamond Turning for Micro-Structured Surfaces

In this paper, adaptive diamond turning (ADT) is proposed for micro-structured surfaces to obtain homogeneous surface quality without loss of efficiency. With consideration of the local shape variation of desired surfaces, the adaptation in feeding direction is to actively control motions along the x-axis to obtain constant scallop heights, whereas the adaptation in forwarding direction is to vary angular sampling intervals to obtain constant linearization errors. The basic principle of ADT is demonstrated, and unique features of the corresponding motions of the machine tool are detailed by numerical simulation. Two degree-of-freedoms fast tool servo or slow slide servo-based turning is strongly recommended to meet the requirements of the ADT method.