111 citations · 113 across the 13 of their papers we have counts for
13 papers
SAFViT: Spatial Attention Fusion Gating for Vision Transformer-Based Nucleus Segmentation and Classification
Harshit Mittal, Arash Rabbani
Accurate cell segmentation and classification are foundational to digital pathology, enabling quantitative tissue analysis for diagnosis and treatment planning. Encoder-decoder arc…
Property-Constrained 3D Porous Media Reconstruction from 2D Images via Conditional Generative Adversarial Networks
Ali Sadeghkhani, Brandon Bennett, Arash Rabbani
This study presents a conditional Generative Adversarial Network (cGAN) framework for generating 3D porous media volumes with controlled porosity, trained exclusively on 2D thin se…
A Decade of Generative Adversarial Networks for Porous Material Reconstruction
Ali Sadeghkhani, Brandon Bennett, Masoud Babaei +1
Digital reconstruction of porous materials has become increasingly critical for applications ranging from geological reservoir characterization to tissue engineering and electroche…
Well Log-Guided Synthesis of Subsurface Images from Sparse Petrography Data Using cGANs
Ali Sadeghkhani, A. Assadi, B. Bennett +1
Pore-scale imaging of subsurface formations is costly and limited to discrete depths, creating significant gaps in reservoir characterization. To address this, we present a conditi…
PCP-GAN: Property-Constrained Pore-scale image reconstruction via conditional Generative Adversarial Networks
Ali Sadeghkhani, Brandon Bennett, Masoud Babaei +1
Obtaining truly representative pore-scale images that match bulk formation properties remains a fundamental challenge in subsurface characterization, as natural spatial heterogenei…
Digital generation of the 3-D pore architecture of isotropic membranes using 2-D cross-sectional scanning electron microscopy images
Sima Zeinali Danalou, Hooman Chamani, Arash Rabbani +3
A major limitation of two-dimensional scanning electron microscopy (SEM) in imaging porous membranes is its inability to resolve three-dimensional pore architecture and interconnec…
On the characteristics of natural hydraulic dampers: An image-based approach to study the fluid flow behaviour inside the human meniscal tissue
J. Waghorne, F. P. Bonomo, A. Rabbani +2
The meniscal tissue is a layered material with varying properties influenced by collagen content and arrangement. Understanding the relationship between structure and properties is…
Rapid Reconstruction of 3-D Membrane Pore Structure Using a Single 2-D Micrograph
Hooman Chamani, Arash Rabbani, Kaitlyn P. Russell +4
Conventional 2-D scanning electron microscopy (SEM) is commonly used to rapidly and qualitatively evaluate membrane pore structure. Quantitative 2-D analyses of pore sizes can be e…
DeepAngle: Fast calculation of contact angles in tomography images using deep learning
Arash Rabbani, Chenhao Sun, Masoud Babaei +3
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 t…
Automated segmentation and morphological characterization of placental histology images based on a single labeled image
Arash Rabbani, Masoud Babaei, Masoumeh Gharib
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…
Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking
Arash Rabbani, Hao Gao, Dirk Husmeier
In this study, we have tailored a pixel tracking method for temporal extrapolation of the ventricular segmentation masks in cardiac magnetic resonance images. The pixel tracking pr…
Resolution enhancement of placenta histological images using deep learning
Arash Rabbani, Masoud Babaei
In this study, a method has been developed to improve the resolution of histological human placenta images. For this purpose, a paired series of high- and low-resolution images hav…
DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials
Arash Rabbani, Masoud Babaei, Reza Shams +2
DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro-tomography images. By combining naturally occurr…