Shape Model Research Articles

A spatiotemporal shape model fitting method for within-season crop phenology detection

Crop phenological information must be reliably acquired earlier in the growing season to benefit agricultural management. Although the popular shape model fitting (SMF) method and its various improved versions (e.g., SMF by the Separate phenological stage, SMF-S) have been successfully applied to after-season crop phenology detection, these existing methods cannot be applied to within-season crop phenology detection. This discrepancy arises due to the fact that, in the within-season scenario, phenological stages can beyond the defined cut-off time. Consequently, enhancing the alignment of the vegetation index (VI) curve segments prior to the cut-off time does not necessarily guarantee accurate within-season phenological detection. To resolve this issue, a new method named spatiotemporal shape model fitting (STSMF) was developed. STSMF does not seek to optimize the local curve matching between the target pixel and the shape model; instead, it determines similar local VI trajectories in the neighboring pixels of previous years. The within-season phenology of the target pixel was thus estimated from the corresponding phenological stage of the determined local VI trajectories. When compared with ground phenology observations, STSMF outperformed the existing SMF and SMF-S which were modified for the within-season scenario (SMFws and SMFSws) with the smallest mean absolute differences (MAE) between observed phenological stages and their corresponding model estimates. The MAE values averaged over all phenological stages for STSMF, SMFSws, and SMFws were 9.8, 12.4, and 27.1 days at winter wheat stations; 8.4, 14.9, and 55.3 days at corn stations; and 7.9, 12.4, and 64.6 days at soybean stations, respectively. Intercomparisons between after-season and within-season regional phenology maps also demonstrated the superior performance of STSMF (e.g., correlation coefficients for STSMF and SMFSws are 0.89 and 0.80 at the maturity stage of winter wheat). Furthermore, the performance of STSMF was less affected by the detection time and the determination of shape models. In conclusion, the straightforward, effective, and stable nature of STSMF makes it suitable for within-season detection of agronomic phenological stages.

Automatic virtual reconstruction of acetabular fractures using a statistical shape model.

Automatic virtual reconstruction of complex fractures would be helpful for pre-operative surgical planning. We developed a statistical shape model (SSM) which contains data of 200 intact 3D hemipelves. It allows for quantification of shape differences and is able to reconstruct abnormal shaped pelvises. We applied our SSM to reconstruct elementary and associate type acetabular fractures and assessed the reconstruction performance of the SSM, by comparing the reconstructed shape with the intact contralateral hemipelvis. In this retrospective diagnostic imaging study, we used our SSM to virtually reconstruct fractured hemipelves of eighty-three patients with an acetabular fracture. A root mean square error (RMSE) was computed between the reconstructed shape and intact contralateral shape for the whole hemipelvis and for regions relevant for plate-fitting. These plate-fitting relevant regions were defined as: (1) Iliopectineal line length and radius; (2) ischial body line length and radius; (3) acetabular diameter, (4) quadrilateral slope and (5) weight-bearing acetabular dome. The median RMSE of the whole hemipelvis of the elementary type fractures was 2.2 (1.7-2.5) mm versus 3.2 (2.2-3.9) mm for the associate type fractures (p < 0.001). The median RMSE for the plate-fitting regions of elementary type fractures was 1.7 (1.4-2.1) mm versus 2.7 (2.0-4.1) mm for associate type fractures (p < 0.001). Using a statistical shape model allows for accurate virtual reconstructions of elementary and associate type acetabular fractures within a clinically acceptable range, especially within regions important for plate-fitting. SSM-based reconstructions can serve as a valuable tool for pre-operative planning in clinical practice, when a template of the contralateral hemipelvis is unavailable.

Mandibular Gender Dimorphism: The Utility of Artificial Intelligence and Statistical Shape Modeling in Skeletal Facial Analysis.

In gender-affirming surgery, facial skeletal dimorphism is an important topic for every craniofacial surgeon. Few cephalometric studies have assessed this topic; however, they fall short to provide skeletal contour insights that direct surgical planning. Herein, we propose statistical shape modeling (SSM) as a novel tool for investigating mandibular dimorphism for young white individuals. A single-center, retrospective study was performed using computed tomography (CT) scans of white individuals, aged 20 to 39 years old. AI-assisted, three-dimensional (3D) mandibles were reconstructed in Materialise Mimics v25.0. We used SSM to generate average 3D models for both genders. Relevant manual anthropometric measurements were taken for the SSMs and individual mandibles. Contour disparities were then represented using 3D overlays and heatmaps. Statistical analyses were performed using unpaired student t testing or Wilcoxon signed rank testing with 95% confidence interval as deemed appropriate by population-level normality assessment. Ninety-eight patients (53 females, 45 males) were included. Male mandibles showed greater bigonial width, intercondylar width, ramus height, and body length [p<0.005]. There was no statistically significant difference in the gonial angle measurements [p=0.62]. All relevant manual individual measurements demonstrated excellent concordance to their SSM counterparts. The 3D overlays of SSMs revealed squarer male chins with more lateral but less anterior projection than their female counterparts. Also, the female mandibles showed smoother transition at the gonial angle. SSM provides a novel tool to objectively evaluate volumetric and contour dimorphisms between genders. Moreover, this method can be automated, allowing for expedited comparisons between populations of interest compared to manual assessment. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 . Bullet points about the importance of this work: Advancing Anthropometric Assessment: Statistical shape modeling (SSM) offers a cutting-edge approach to visualizing gender-specific skeletal anatomic differences for aesthetic and gender-affirming facial surgery. Expediting Comparative Analysis: The workflow established in this paper streamlines the evaluative process, enabling rapid morphologic comparisons between populations. Patient-Centered Care: This study establishes a foundation for the development of SSMs in individualized operative planning.

A Hierarchical Neural Network for Point Cloud Segmentation and Geometric Primitive Fitting.

Automated generation of geometric models from point cloud data holds significant importance in the field of computer vision and has expansive applications, such as shape modeling and object recognition. However, prevalent methods exhibit accuracy issues. In this study, we introduce a novel hierarchical neural network that utilizes recursive PointConv operations on nested subdivisions of point sets. This network effectively extracts features, segments point clouds, and accurately identifies and computes parameters of regular geometric primitives with notable resilience to noise. On fine-grained primitive detection, our approach outperforms Supervised Primitive Fitting Network (SPFN) by 18.5% and Cascaded Primitive Fitting Network (CPFN) by 11.2%. Additionally, our approach consistently maintains low absolute errors in parameter prediction across varying noise levels in the point cloud data. Our experiments validate the robustness of our proposed method and establish its superiority relative to other methodologies in the extant literature.

Aneurysm growth evaluation and detection: a computer-assisted follow-up MRA analysis

Growing intracranial aneurysms pose a high risk of rupture, making the detection and quantification of the growth crucial for timely treatment strategy adoption. In this paper we propose a computer-assisted approach based on the extraction of IA shapes from associated baseline and follow-up angiographic scans and non-rigid morphing of the two shapes. From the obtained shape deformations we computed four novel features, including differential volume (dV), surface area (dSA), aneurysm-size normalized median deformation path length (dMPL), and integral of cumulative deformation distances (dICDD). An experienced neuroradiologist manually extracted the IA shape models from the baseline and follow-up MRAs and, by utilizing size change and visual assessments, classified each aneurysm into stable with morphology changes, stable or growing. We investigated the classification performance and found that three of the novel and one cross-sectional feature exhibited significantly different mean values (p-value <0.05\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$< 0.05$$\\end{document}; Tukey’s HSD test) between the stable and growing IA groups, while the mean dICDD was significantly different between all the three groups. The cross-sectional features has sensitivity to growing IAs in range 0.05–0.86, while novel features had generally higher sensitivity in range 0.81–0.90, making them promising candidates as surrogate follow-up imaging-based biomarkers for IA growth detection. These findings may offer valuable information for clinical management of patients with IAs based on follow-up imaging.

Improved identification and monitoring of meteorological, agricultural, and hydrological droughts using the modified nonstationary drought indices in the Yellow River Basin of China

Non-stationarity in hydro-meteorological series complicates drought monitoring and assessment. Our study aimed to better identify and monitor meteorological, agricultural and hydrological droughts in the Yellow River Basin (YRB) of China. We constructed nonstationary standardized precipitation index (NSPI), nonstationary standardized precipitation evapotranspiration index (NSPEI), nonstationary standardized soil moisture index (NSSMI) and nonstationary standardized runoff index (NSRI) based on the Generalized Additive Models for Location, Scale, and Shape (GAMLSS). We also investigated the performance of NSPI/NSPEI/NSSMI/NSRI by comparing them with historical drought events. Our results revealed that: (1) The probability distribution function (PDF), smoothing function (SF), and degrees of freedom (DF) significantly influenced the fitting effect of the GAMLSS model. The PDF emerged as the most critical factor in determining fitting accuracy, while SF and DF played crucial roles in preventing overfitting and accurately depicting data variations. (2) Stationary GAMLSS models were primarily suited for time series with lower-scales (1- and 3-month scales), whereas nonstationary models exhibited better suitability for time series with higher-scale (6- and 12-month scales). (3) NSPI and NSPEI demonstrated heightened sensitivity in identifying meteorological droughts, with NSPEI showing greater precision in determining drought intensity and duration. However, compared to SSMI, while NSSMI aligned more closely with historical agricultural drought events, it exhibited limited responsiveness to mild and moderate drought. (4) NSRI indicated that hydrological droughts in the tributaries of the YRB were significantly influenced by meteorological droughts, and the intensity of hydrological drought was stronger than that in the mainstream of the YRB. In conclusion, the nonstationary drought indices could better identify drought events in the period of climate change, and provide valuable information for drought management in the climate change environment.

OpenECAD: An efficient visual language model for editable 3D-CAD design

Computer-aided design (CAD) tools are utilized in the manufacturing industry for modeling everything from cups to spacecraft. These programs are complex to use and typically require years of training and experience to master. Structured and well-constrained 2D sketches and 3D constructions are crucial components of CAD modeling. A well-executed CAD model can be seamlessly integrated into the manufacturing process, thereby enhancing production efficiency. Deep generative models of 3D shapes and 3D object reconstruction models have garnered significant research interest. However, most of these models produce discrete forms of 3D objects that are not editable. Moreover, the few models based on CAD operations often have substantial input restrictions. In this work, we fine-tuned pre-trained models to create OpenECAD models (0.55B, 0.89B, 2.4B and 3.1B), leveraging the visual, logical, coding, and general capabilities of visual language models. OpenECAD models can process images of 3D designs as input and generate highly structured 2D sketches and 3D construction commands, ensuring that the designs are editable. These outputs can be directly used with existing CAD tools’ APIs to generate project files. To train our network, we created a series of OpenECAD datasets. These datasets are derived from existing public CAD datasets, adjusted and augmented to meet the specific requirements of vision language model (VLM) training. Additionally, we have introduced an approach that utilizes dependency relationships to define and generate sketches, further enriching the content and functionality of the datasets.

Probe the Regolith Characteristics of Asteroids from 9 yr Infrared Observations of WISE/NEOWISE: A Case Study of The Main-belt Object (656) Beagle

This work presents the data processing, fitting procedure, modeling, and analyzing of 9 yr infrared light curves provided by the WISE/NEOWISE telescope, by which the regolith characteristics of Main-belt Object (656) Beagle is studied. Beagle locates in the Themis family, and was proposed to be the parent of the first main-belt comet (MBC) 133P. We determine Beagle’s effective diameter Deff=57.3−2.2+4.5 km, geometric albedo pv=0.05−0.007+0.004 , mean roughness θ RMS = 44 ± 4°, mean grain size b=100−90+350μm , mean specific heat capacity c p = 173 ∼ 516 J Kg−1 K−1, mean thermal conductivity κ = 0.7 ∼ 1.3 × 10−3 W m−1 K−1, and mean thermal inertia Γ = 14 ∼ 32 J m−2 s−0.5 K−1. The albedo of Beagle is a little anomalous that the albedos of Beagle’s neighboring asteroids are more close to (24) Themis, rather than Beagle itself. The W1-band near-infrared (NIR) light curves do not reveal significant heterogeneous NIR features on the surface of Beagle, being inconsistent with the expectation of a family parent that has members with diverse NIR spectral types. These results add new clues of Beagle probably being an interloper or a sister, rather than the parent of its neighboring asteroids including the famous MBC 133P, hence may lead to new scenarios about the origin of 133P. Besides, we found that asteroidal shape models from inversion of optical light curves are imperfect for modeling infrared lightcurves, thus could mislead evaluations of both the heterogeneity of regolith reflectivity at NIR and thermophysical characteristics at thermal infrared.

Using A Disentangled Neural Network to Objectively Assess the Outcomes of Midfacial Surgery in Syndromic Craniosynostosis.

Advancements in artificial intelligence and the development of shape models that quantify normal head shape and facial morphology provide frameworks by which the outcomes of craniofacial surgery can be compared. In this work, we will demonstrate the use of the Swap Disentangled Variational Autoencoder (SD-VAE) to objectively assess changes following midfacial surgery. Our model is trained on a dataset of 1405 3D meshes of healthy and syndromic patients which was augmented using a technique based on spectral interpolation. Patients with a diagnosis of Apert and Crouzon syndrome who had undergone sub- or trans-cranial midfacial procedures utilising rigid external distraction were then interpreted using this model as the point of comparison. A total of 56 patients met our inclusion criteria, 20 with Apert and 36 with Crouzon syndrome. By using linear discriminant analysis to project the high-dimensional vectors derived by SD-VAE onto a 2D space, the shape properties of Apert and Crouzon syndrome can be visualised in relation to the healthy population. In this way, we are able to show how surgery elicits global shape changes in each patient. To assess the regional movements achieved during surgery, we use a novel metric derived from the Malahanobis distance to quantify movements through the latent space. Objective outcome evaluation, which encourages in-depth analysis and enhances decision making, is essential for the progression of surgical practice. We have demonstrated how artificial intelligence has the ability to improve our understanding of surgery and its effect on craniofacial morphology.

An accessible and intuitive approach to 3D data modeling for morphometric surface evaluation in pig farming

The recording of pig surfaces allows for the estimation of growth-related body size dimensions from which the animals’ condition can be inferred. Furthermore, for purposes such as animal breeding, it is of particular interest to describe the shape variations within the population. Therefore, we built a Statistical Shape Model (SSM) that enables a quantitative and visual exploration of variation in size, form and posture in an intuitive way.We included 4315 images from 582animals with a body weight ranging from 50 to 140 kg. After data processing, i.e. the removal of data points corresponding to the head and tail, the 3D point clouds were reduced to an equal number of reference points. These reference points were arranged so that the pig surface could be reconstructed via Bézier surface patches. Then, Procrustes Analysis was performed to align the reference points from all shapes. A mean shape and its main modes of variation were derived, some of which were used to correct the posture of the animals.A linear regression for weight prediction resulted in a mean absolute error of 5.06 kg before and 2.84 kg after data processing. The median error of surface reconstruction was 2.5 mm taking eight surface patches with 45 reference points and 1.3 mm taking 32 surface patches with 153 reference points into account. The first six modes of variation from the SSM indicated differences in size and posture. Interestingly, regions prone to noise were also revealed.In conclusion, we present a novel approach that can be used to receive a compact number of corresponding data points, providing data that is easier and more efficient to handle while exposing shape-specific attributes. Our approach is helpful to visualize and compare shapes, including the collection of body size measurements as well as unsupervised and supervised clustering for more objective data-driven decision-making.

KRC-APM: Key region cutting and artificial prior model for breast cancer recognition in ultrasound images

Computer-aided analysis of ultrasound images is important for the early detection of breast cancer, obtaining more treatment time and improving the likelihood of survival. Current methods are either limited by predefined Regions of Interest (ROIs), suffer from a low proportion and variable locations of tumors, or lack appropriate modeling of tumor characteristics in ultrasound images. This paper proposes a framework called Key Region Cutting and Artificial Prior Model (KRC-APM) for breast cancer recognition in ultrasound images. Firstly, to avoid the reliance on predefined ROIs, as well as to increase the proportion of tumor areas and eliminate abundant background, we propose a Key Region Cutting (KRC) method, which analyzes the confidence levels for tumor areas and tolerantly cuts high-confidence areas into key regions. Next, to model the characteristics of tumors in ultrasound images, we design three types of diagnosis-related Artificial Priors (APs) under the guidance of experienced oncologists: Shape Modeling, Margin Analysis, and Echogenicity Pattern Indication. Finally, to fuse the key region with the extracted APs, we propose an Artificial Prior Model (APM), which synthetically learns the pattern of breast cancer. The proposed KRC-APM is validated on two public datasets. The experimental results demonstrate that the key regions identified by the KRC method and the designed APs significantly enhance the performance of breast cancer recognition. Furthermore, the proposed KRC-APM framework outperforms current state-of-the-art (SOTA) methods.

Collision-induced absorptions by pure CO2 in the infrared: New measurements in the 1150–4500 cm−1 spectral range and empirical modeling for applications

Knowledge of collision-induced absorptions (CIA) by pure CO2 for wide temperature ranges is necessary for atmospheric and climate studies of planets and exoplanets with CO2-rich atmospheres. In this work, using spectra measured with a Fourier transform spectrometer for temperatures between 230 K and 295 K at pressures up to 40 bar, several CIA bands in the 1150–4500 cm−1 spectral range have been determined, in some cases for the first time. The results are compared with values reported in the literature, showing good agreements. From data obtained in this work and those in the literature, we propose simple analytical models for the temperature dependent band shape and integrated band intensity (IBI) which enable the calculation of all investigated CIA transitions in a wide temperature range.

Temperature dependence of line shape parameters for N2- and O2-broadened methane lines by quantum cascade laser spectroscopy

This paper report nitrogen- and oxygen-broadening coefficients, their speed-dependencies, and the collisional narrowing parameters of lines in the ν4-band of methane. The parameters were determined from low (150 K) to high temperatures (600 K). The measurements were done with a high-resolution quantum cascade laser spectrometer coupled to specific absorption cells that allow to cool and heat the gas mixtures with a great stability. The spectroscopic parameters were determined at each temperature by fits of the experimental absorbances in a multispectrum fitting procedure using the Voigt, Speed-Dependent Voigt, Nelkin-Ghatak and Speed-dependent Nelkin-Ghatak theoretical line shape models. The temperature dependencies of the collisional half-widths, the speed-dependence of the broadening coefficients as well as the collisional narrowing parameters were studied with the empirical power law and the physics-based Gamache-Vispoel model (DPL) [Gamache and Vispoel, JQSRT, 217, 440–452, 2018]. The obtained line shape parameters and their temperature dependencies are compared, when possible, to existing data in literature. The DPL reproduces more accurately the evolution of the studied collisional parameters over the wide range of temperature considered in the study.

Distributional modelling of positively skewed data via the flexible Weibull extension distribution

SummaryThe time until an event occurs is often known to have a skewed distribution. To model this, a statistical distribution called the two‐parameter flexible Weibull extension (FWE) has been proposed. In this paper, the FWE distribution is used to model datasets through the use of generalised additive models for location, scale and shape (GAMLSS) distributional regression. GAMLSS is the only regression technique that can examine the effects of both categorical and numeric predictors on all the parameters of the distribution used to fit the dependent variable. To make it easier to use the FWE distribution through GAMLSS, the RelDists R package is proposed. A simulation study shows that FWE modelling through GAMLSS provides reliable parameter estimates even in the presence of factors that affect the distribution.

Virtual assessment of internal rotation in reverse shoulder arthroplasty based on statistical shape models of scapular size

Virtual assessment of internal rotation in reverse shoulder arthroplasty based on statistical shape models of scapular size

MENOPAUSE AND PARITY ARE ASSOCIATED WITH LUMBAR SPINE CURVATURE

BackgroundLateral lumbar spine statistical shape models (SSM) have been used previously to describe associations with osteoarthritis and back pain. However, associations with factors such as osteoporosis, menopause and parity have not been explored.Methods and ResultsA 143-point SSM, describing L1 to the top of L5, was applied to lateral spine iDXA scans from UK Biobank. Associations with self-reported osteoporosis, menopause, parity and back pain and the first 10 modes of variation were examined using adjusted binary logistic regression or linear regression (adjusted for age, height, weight and total spine BMD). We report odds ratios with 95% confidence intervals for each standard deviation change in mode.Complete data were available for 2494 women. Mean age was 61.5 (± 7.4) years. 1369 women reported going through menopause, 96 women self-reported osteoporosis and 339 women reported chronic back pain. 80% of women reported at least 1 live birth.Lumbar spine shape was not associated with back pain in this cohort. Two modes were associated with menopause (modes 1 &amp; 2), 1 mode with parity (mode 1) and 2 modes with osteoporosis (modes 3 &amp; 5). Mode 1 (43.6% total variation), describing lumbar curvature was positively associated with both menopause [OR 1.15 95% CI 1.00–1.33, p=0.05] and parity [OR 1.058 95% CI 1.03–1.0, p=0.01]. Mode 3, describing decreased vertebral height was positively associated with osteoporosis [OR 1.40 95% CI 1.14–1.73, p=0.001].ConclusionMenopause and parity were associated with a curvier lumbar spine and osteoporosis with decreased vertebral height. Shape was not associated with back pain.No conflicts of interest Sources of fundingWellcome Trust collaborative award ref 209233

Interactive Mesh Sculpting with Arbitrary Topologies in Head-Mounted VR Environments

Shape modeling is a dynamic area in computer graphics with significant applications in computer-aided design, animation, architecture, and entertainment. Virtual sculpting, a key paradigm in free-form modeling, has traditionally been performed on desktop computers where users manipulate meshes with controllers and view the models on two-dimensional displays. However, the advent of Extended Reality (XR) technology has ushered in immersive interactive experiences, expanding the possibilities for virtual sculpting across various environments. A real-time virtual sculpting system implemented in a Virtual Reality (VR) setting is introduced in this paper, utilizing quasi-uniform meshes as the foundational structure. In our innovative sculpting system, we design an integrated framework encompassing a surface selection algorithm, mesh optimization technique, mesh deformation strategy, and topology fusion methodology, which are all tailored to meet the needs of the sculpting process. The universal, user-friendly sculpting tools designed to support free-form topology are offered in this system, ensuring that the meshes remain watertight, manifold, and free from self-intersections throughout the sculpting process. The models produced are versatile and suitable for use in diverse fields such as gaming, art, and education. Experimental results confirm the system’s real-time performance and universality, highlighting its user-centric design.

Machine Learning and Statistical Shape Modelling Methodologies to Assess Vascular Morphology before and after Aortic Valve Replacement.

Introduction: Statistical shape modelling (SSM) is used to analyse morphology, discover qualitatively and quantitatively unique shape features within a population, and generate mean shapes and shape modes that show morphological variability. Hierarchical agglomerative clustering is a machine learning analysis used to identify subgroups within a given population in relation to shape features. We tested the application of both methods in the clinically relevant scenario of patients undergoing aortic valve repair (AVR). Every year, around 5000 patients undergo surgical AVR in the UK. Aims: Evaluate aortic morphology and identify subgroups amongst patients who had undergone AVR, including Ozaki, Ross, and valve-sparing procedures using SSM and unsupervised hierarchical clustering analysis. This methodological framework can evaluate both pre- and post-surgical variability across subgroups undergoing different surgeries. Methods: Pre- (n = 47) and post- (n = 35) operative three-dimensional (3D) aortic models were reconstructed from computed tomography (CT) and cardiac magnetic resonance (CMR) images. Computational analyses for SSM and hierarchical clustering were run separately for the two subgroups, assessing (a) ascending aorta only and (b) the whole aorta. This allows for exploring possible variations in morphological classification related to the input shape. Results: Most patients in the Ross procedure subgroup exhibited differences in aortic morphology from other subgroups, including an elongated ascending and wide aortic arch pre-operatively, and an elongated ascending aorta with a slightly enlarged sinus post-operatively. In hierarchical clustering, the Ross aortas also appeared to cluster together compared to the other surgical procedures, both pre-operatively and post-operatively. There were significant differences between clusters in terms of clustering distance in the pre-operative analyses (p = 0.003 for ascending aortas, p = 0.016 for whole aortas). There were no significant differences between the clusters in post-operative analyses (p = 0.47 for ascending, p = 0.19 for whole aorta). Conclusions: We demonstrated the feasibility of evaluating aortic morphology before and after different aortic valve surgeries using SSM and hierarchical clustering. This framework could be used to further explore shape features associated with surgical decision-making pre-operatively and, importantly, to identify subgroups whose morphology is associated with poorer clinical outcomes post-operatively. Statistical shape modelling (SSM) and unsupervised hierarchical clustering are two statistical methods that can be used to assess morphology, show morphological variations, with the latter being able to identify subgroups within a population. These methods have been applied to the population of aortic valve replacement (AVR) patients since there are different surgical procedures (traditional AVR, Ozaki, Ross, and valve-sparing). The aim is to evaluate aortic morphology and identify subgroups within this population before and after surgery. Computed tomography and cardiac magnetic resonance images were reconstructed into 3D models of the ascending aorta and whole aorta, which were then input into SSM and hierarchical clustering. The results show that the Ross aortic morphology is quite different from the other aortas. The clustering did not classify the aortas based on the surgical procedures; however, most of the Ross group did cluster together, indicating low variability within this surgical group.

Heatmap-Based Active Shape Model for Landmark Detection in Lumbar X-ray Images.

Medical staff inspect lumbar X-ray images to diagnose lumbar spine diseases, and the analysis process is currently automated using deep-learning techniques. The detection of landmarks is necessary in the automatic process of localizing the position and identifying the morphological features of the vertebrae. However, detection errors may occur owing to the noise and ambiguity of images, as well as individual variations in the shape of the lumbar vertebrae. This study proposes a method to improve the robustness of landmark detection results. This method assumes that landmarks are detected by a convolutional neural network-based two-step model consisting of Pose-Net and M-Net. The model generates a heatmap response to indicate the probable landmark positions. The proposed method then corrects the landmark positions using the heatmap response and active shape model, which employs statistical information on the landmark distribution. Experiments were conducted using 3600 lumbar X-ray images, and the results showed that the landmark detection error was reduced by the proposed method. The average value of maximum errors decreased by 5.58% after applying the proposed method, which combines the outstanding image analysis capabilities of deep learning with statistical shape constraints on landmark distribution. The proposed method could also be easily integrated with other techniques to increase the robustness of landmark detection results such as CoordConv layers and non-directional part affinity field. This resulted in a further enhancement in the landmark detection performance. These advantages can improve the reliability of automatic systems used to inspect lumbar X-ray images. This will benefit both patients and medical staff by reducing medical expenses and increasing diagnostic efficiency.

Assessment of dynamic hydrological drought risk from a non‐stationary perspective

AbstractThe stationarity hypothesis of hydrometeorological elements has been questioned in the context of global warming and intense human disturbance. The conventional drought index and methods of frequency analysis may no longer be applicable for hydrological drought risk evaluation under a changing environment. In this study, a new dynamic hydrological drought risk evaluation framework is proposed for application to the Hanjiang River basin (HRB), which simultaneously considers the non‐stationarity in the construction of drought index as well as in the frequency analysis. First, a non‐stationary standardized runoff index (NSRI) is developed using a generalized additive model for location, scale and shape (GAMLSS) framework. Then, hydrological drought characteristics including duration and severity are identified, and their marginal distributions are established. Finally, based on the dynamic copula, considering the non‐stationarity of the dependence structure, the dynamic joint probability distribution, conditional probability distribution and return period of the bivariate hydrological drought properties are analysed. Results showed that NSRI, which integrates the impacts of climate change and anthropogenic activities on the non‐stationarity of runoff series, had a better ability to capture runoff extremes than had SRI. In addition, it is indispensable to consider the non‐stationarity of the dependence structure between variables when discussing the multivariate joint risk of hydrological drought. The risk of hydrological drought in the study area has shown an increasing trend in the past 65 years, and the drought conditions from upstream to downstream have been alleviated first and then intensified. This study provides valuable information for regional drought risk estimation and water resources management from a non‐stationary perspective.