Automated segmentation and morphological characterization of placental histology images based on a single labeled image

In this study, a novel method of data augmentation has been presented for the segmentation of placental histological images when the labeled data are scarce. This method generates new realizations of the placenta intervillous morphology while maintaining the general textures and orientations. As a result, a diversified artificial dataset of images is generated that can be used for training deep learning segmentation models. We have observed that on average the presented method of data augmentation led to a 42% decrease in the binary cross-entropy loss of the validation dataset compared to the common approach in the literature. Additionally, the morphology of the intervillous space is studied under the effect of the proposed image reconstruction technique, and the diversity of the artificially generated population is quantified. Due to the high resemblance of the generated images to the real ones, the applications of the proposed method may not be limited to placental histological images, and it is recommended that other types of tissues be investigated in future studies

DeepAngle: Fast calculation of contact angles in tomography images using deep learning

DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials. Measurement of angles in 3--D needs to be done within the surface perpendicular to the angle planes, and it could become inaccurate when dealing with the discretized space of the image voxels. A computationally intensive solution is to correlate and vectorize all surfaces using an adaptable grid, and then measure the angles within the desired planes. On the contrary, the present study provides a rapid and low-cost technique powered by deep learning to estimate the interfacial angles directly from images. DeepAngle is tested on both synthetic and realistic images against the direct measurement technique and found to improve the r-squared by 5 to 16% while lowering the computational cost 20 times. This rapid method is especially applicable for processing large tomography data and time-resolved images, which is computationally intensive. The developed code and the dataset are available at an open repository on GitHub (https://www.github.com/ArashRabbani/DeepAngle)

CO2-plume geothermal: Power net generation from 3D fluvial aquifers

Previously CO2, as a heat-extraction fluid, has been proposed as a superior substitute for brine in geothermal energy extraction. Hence, the new concept of CO2-plume geothermal (CPG) is suggested to generate heat from geothermal aquifers using CO2 as the working fluid. In January 2015, a CPG-thermosiphon system commenced at the SECARB Cranfield Site, Mississippi. By utilising CO2, the demand for the pumping power is greatly reduced due to the thermosiphon effect at the production well. However, there are still parameters such as aquifer thermal depletion, required high injection rates, and CO2-plume establishment time, that hinder CPG from becoming viable. Moreover, the fluvial nature of sedimentary aquifers significantly affects the heat and mass transfer inside the aquifer, as well as the system performance. In the present study, a direct-CO2 thermosiphon system is considered that produces electricity from a 3D braided-fluvial sedimentary aquifer by providing an excess pressure at the surface that is used in the turbine. The system performance and net power output are analyzed in 15 3D fluvial heterogeneous - with channels’ widths of 50, 100, and 150 m - and three homogeneous aquifer realizations with different CO2 injection rates. It is observed that the presence of fluvial channels significantly increases the aquifer thermal depletion pace (22-120%) and therefore, reduces the system’s performance up to about 75%. Additionally, it is found that the CPG system with the CO2 injection rate of 50 kg/s and the I-P line parallel to the channels provides the maximum cycle operation time (44 years), as well as the optimum performance for the heterogeneous cases of the present study by providing about 0.06-0.12 TWh energy during the simulation time of 50 years. Also, to prevent rapid drops in excess pressure, a system with a yearly adjustable injection rate is implemented, which prevents the production well bottomhole temperature to fall below 80 ◦C

Graph Neural Network Emulation of Cardiac Mechanics

This paper compares the performance of two graph neural network architectures on the emulation of a cardiac mechanic model of the left ventricle of the heart. These models can be applied directly on the same computational mesh of the left ventricle geometry that is used by the expensive numerical forward solver, precluding the need for a low-order approximation of the true geometry. Our results show that these emulation approaches incur negligible loss in accuracy compared in the forward simulator, while making predictions multiple orders of magnitude more quickly, raising the prospect for their use in both forward and inverse problems in cardiac modelling

Bayesian inference of cardiac models emulated with a time series Gaussian process

The objective of this research is to estimate the specific biophysical parameters of a cardiac mechanics model using a time series of variables that can be acquired in the clinic. This method is driven by the need to infer the passive stiffness of the myocardium to diagnose cardiophysiological diseases, which requires the measurement of the volume of the left ventricle (LV) of the heart at different time points. Although there have been many advancements in cardiac models, their computation is complex and costly. To overcome this challenge, we propose a method that utilizes a Gaussian process to construct a statistical model for emulation. Since the LV volumes are acquired in a time series, we employ the Kronecker product to compute two covariance matrices separately for time and biophysical parameters. Once we construct an accurate emulator to represent the passive filling process of the cardiac mechanics model during diastole, we can estimate the biophysical parameters inversely. We also aim to evaluate the impact of increasing the number of time points on reducing the uncertainty of the inverse estimation in this study

On the characteristics of natural hydraulic dampers : an image-based approach to study the fluid flow behaviour inside the human meniscal tissue

The meniscal tissue is a layered material with varying properties influenced by collagen content and arrangement. Understanding the relationship between structure and properties is crucial for disease management, treatment development, and biomaterial design. The internal layer of the meniscus is softer and more deformable than the outer layers, thanks to interconnected collagen channels that guide fluid flow. To investigate these relationships, we propose an integrated approach that combines Computational Fluid Dynamics (CFD) with Image Analysis (CFD-IA). We analyze fluid flow in the internal architecture of the human meniscus across a range of inlet velocities (0.1 mm/s to 1.6 m/s) using high-resolution 3D micro-computed tomography scans. Statistical correlations are observed between architectural parameters (tortuosity, connectivity, porosity, pore size) and fluid flow parameters (number distribution, permeability). Some channels exhibit values of 1400 at an inlet velocity of 1.6 m/s, and a transition from Darcy’s regime to a non-Darcian regime occurs around an inlet velocity of 0.02 m/s. Location-dependent permeability ranges from 20-32 Darcy. Regression modelling reveals a strong correlation between fluid velocity and tortuosity at high inlet velocities, as well as with channel diameter at low inlet velocities. At higher inlet velocities, flow paths deviate more from the preferential direction, resulting in a decrease in the concentration parameter by an average of 0.4. This research provides valuable insights into the fluid flow behaviour within the meniscus and its structural influences. 3D models and image stack are available to download at https://doi.org/10.5281/zenodo.10401592