An End-to-End Deep Learning Generative Framework for Refinable Shape Matching and Generation

Generative modelling for shapes is a prerequisite for In-Silico Clinical Trials (ISCTs), which aim to cost-effectively validate medical device interventions using synthetic anatomical shapes, often represented as 3D surface meshes. However, constructing AI models to generate shapes closely resembling the real mesh samples is challenging due to variable vertex counts, connectivities, and the lack of dense vertex-wise correspondences across the training data. Employing graph representations for meshes, we develop a novel unsupervised geometric deep-learning model to establish refinable shape correspondences in a latent space, construct a population-derived atlas and generate realistic synthetic shapes. We additionally extend our proposed base model to a joint shape generative-clustering multi-atlas framework to incorporate further variability and preserve more details in the generated shapes. Experimental results using liver and left-ventricular models demonstrate the approach's applicability to computational medicine, highlighting its suitability for ISCTs through a comparative analysis

Vascular Tree Tracking and Bifurcation Points Detection in Retinal Images Using a Hierarchical Probabilistic Model

Background and Objective Retinal vascular tree extraction plays an important role in computer-aided diagnosis and surgical operations. Junction point detection and classification provide useful information about the structure of the vascular network, facilitating objective analysis of retinal diseases. Methods In this study, we present a new machine learning algorithm for joint classification and tracking of retinal blood vessels. Our method is based on a hierarchical probabilistic framework, where the local intensity cross sections are classified as either junction or vessel points. Gaussian basis functions are used for intensity interpolation, and the corresponding linear coefficients are assumed to be samples from class-specific Gamma distributions. Hence, a directed Probabilistic Graphical Model (PGM) is proposed and the hyperparameters are estimated using a Maximum Likelihood (ML) solution based on Laplace approximation. Results The performance of proposed method is evaluated using precision and recall rates on the REVIEW database. Our experiments show the proposed approach reaches promising results in bifurcation point detection and classification, achieving 88.67% precision and 88.67% recall rates. Conclusions This technique results in a classifier with high precision and recall when comparing it with Xu’s method

Graph-based probabilistic geometric deep learning framework for generation of virtual anatomical populations

In the field of medical imaging, "shape generation" refers to the computational techniques employed to create accurate and detailed representations of anatomical structures/organs. Shape generation plays a crucial role in medical imaging, profoundly impacting clinical applications and diagnostics. Accurate representation of anatomical structures is essential for disease detection, treatment planning, personalized medicine, and computational modelling. Leveraging machine learning and computational modelling opens avenues for valuable insights through In-Silico Clinical Trials (ISCTs). In ISCTs, virtual populations of anatomical shapes are vital for evaluating clinical devices. These populations must capture anatomical and physiological variability while remaining plausible to ensure meaningful and reliable results. By generating virtual shape populations, researchers can simulate and assess medical interventions, accelerating the development of improved therapies and devices. However, constructing generative models faces challenges due to the fact that real-world anatomical shapes, derived from different subjects, exhibit varying topological structures and, in general, there is no topological correspondence among shapes from different subjects. This thesis aims to address the challenges associated with shape matching and generation by introducing an unsupervised probabilistic deep generative model, applicable to datasets including shape surface mesh data with varying topological structures and the absence of correspondences. The proposed framework leverages graph representations to capture the geometric characteristics of anatomical shapes and incorporates advanced techniques in geometric deep learning. By employing these algorithms, the framework is able to establish a learnable set of vertex-wise correspondences between shapes in the latent space while learning/constructing a population-derived atlas model. Subsequently, the model generates virtual populations of anatomical shapes that closely resemble real-world data. This novel generative framework is designed to handle variable mesh topology across patients/input shapes and successfully synthesises anatomically plausible virtual populations with significant variability in shape and diverse topologies. These capabilities expand the potential applications of the approach in computational medicine and make it well-suited for ISCTs

Steady State Simulation of Two-Gas Phase Fluidized Bed Reactors in Series for Producing Linear Low Density Polyethylene

A linear low density polyethylene (LLDPE) production process, including two- fuidized bed reactors in series (FBRS) and other process equipment, was completely simulated by Aspen Polymer Plus software. Fluidized bed reactors were considered as continuous stirred tank reactors (CSTR consisted of polymer and gas phases). POLY-SRK and NRTL-RK equations of state were used to describe polymer and non-polymer streams, respectively. In this simulation, a kinetic model, based on a double active site heterogeneous Ziegler-Natta catalyst was used for simulation of LLDPE process consisting of two FBRS. Simulator using this model has the capability to  predict a number of  principal characteristics of LLDPE such as melt fow index (MFI), density, polydispersity index, numerical and weight average molecular weights (Mn,Mw) and copolymer molar fraction (SFRAC). The results of the simulation were compared with industrial plant data and a good agreement was observed between the predicted model and plant data. The simulation results show the relative error of about 0.59% for prediction of polymer mass fow and 2.67% and 0.04% for prediction of product MFI and density, respectively

A Geometric Deep Learning Framework for Generation of Virtual Left Ventricles as Graphs

Generative statistical models have a wide range of applications in the modelling of anatomies. In-silico clinical trials of medical devices, for instance, require the development of virtual populations of anatomy that capture enough variability while remaining plausible. Model construction and use are heavily influenced by the correspondence problem and establishing shape matching over a large number of training data. This study focuses on generating virtual cohorts of left ventricle geometries resembling different-sized shape populations, suitable for in-silico experiments. We present an unsupervised data-driven probabilistic generative model for shapes. This framework incorporates an attention-based shape matching procedure using graph neural networks, coupled with a β−VAE generation model, eliminating the need for initial shape correspondence. Left ventricle shapes derived from cardiac magnetic resonance images available in the UK Biobank are utilized for training and validating the framework. We investigate our method's generative capabilities in terms of generalisation and specificity and show that it is able to synthesise virtual populations of realistic shapes with volumetric measurements in line with actual clinical indices. Moreover, results show our method outperforms joint registration-PCA-based models.</p

Business Owners’ Feedback toward Adoption of Open Data: A Case Study in Kuwait

The world intention toward open data technology has increased in the past years, and governments started to explore open data technology in the public and private sectors and tried to check its advantages and disadvantages. However, in the Arab world and especially in Kuwait, there is no solid structured attention about the technology in both sectors. As a result, we tried in this paper to determine if business owners in Kuwait have enough knowledge of the open data (OD) concept and if they have the willingness to use it for enhancing their services. The purpose of this research is to measure the acceptance of OD technology in Kuwait and to gather business owners' opinions about the ability to adopt the OD concept. Making online and hardcopy survey was our method for gathering different reactions and points of view about this technology. We intended to focus on the private sector and we targeted people who own a business and wish to introduce better services for their customers. Overall, the results have shown clear features about open data technology in Kuwait and the substantial need of education and awareness of the importance of this technology. The results of this study may positively and directly affect the level of motivation for other existing studies

Dynamic recrystallization under hot deformation of additively manufactured 316 L stainless steel

The high-temperature deformation behavior of additively manufactured 316 L stainless steels prepared via the laser powder bed fusion (LPBF) technology was investigated using Gleeble compression testing in a wide temperature range of 473–1273 K under a constant strain rate of 0.001 s−1, perpendicular to the building direction (BD). The softening flow property upon continuous concurrent isothermal heating deformation displayed an excellent thermal stability performance up to the temperature of 873 K, preserving the material strength higher than 600 MPa compared to the room-temperature property of the as-built material. By the gradual increase of testing temperature, the compressive strength of the material was continuously decreased to even \u3c50 MPa at a temperature of 1273 K. After hot compression; all tested specimens were characterized across different regions in terms of deformation substructure evolution and crystallographic texture using electron backscattering diffraction (EBSD) and constitutive analyses. Accordingly, the estimated activation energies based on established constitutive modeling proposed the operation of dynamic recovery (DRV) and discontinuous dynamic recrystallization (DDRX) mechanisms for the hot deformation of additively manufactured 316 L stainless steel below (∼[Formula presented]) and higher (∼[Formula presented]) than the critical temperature of 873 K, respectively. The competition between strain accumulation and thermal heating induced two different Brass deformation and Goss recrystallization textures below and above this critical temperature by altering the operative dominant dynamic restoration mechanism, with various texture severity depending on the testing temperature