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

1 code implementation • 28 Nov 2022 • Arash Rabbani, Chenhao Sun, Masoud Babaei, Vahid J. Niasar, Ryan T. Armstrong, Peyman Mostaghimi

DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials.

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

1 code implementation • 7 Oct 2022 • Arash Rabbani, Masoud Babaei, Masoumeh Gharib

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.

Data Augmentation Image Reconstruction +1

Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking

1 code implementation • 30 Jul 2022 • Arash Rabbani, Hao Gao, Dirk Husmeier

The pixel tracking process starts from the end-diastolic frame of the heart cycle using the available manually segmented images to predict the end-systolic segmentation mask.

Segmentation Superpixels

Resolution enhancement of placenta histological images using deep learning

1 code implementation • 30 Jul 2022 • Arash Rabbani, Masoud Babaei

For this purpose, a paired series of high- and low-resolution images have been collected to train a deep neural network model that can predict image residuals required to improve the resolution of the input images.

DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials

1 code implementation • 3 May 2020 • Arash Rabbani, Masoud Babaei, Reza Shams, Ying Da Wang, Traiwit Chung

DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro-tomography images.

Physical Simulations