Publications

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2023
Bibars, M., P. E. Salah, A. Eldeib, M. A. Elattar, and I. A. Yassine, "Cross-Modality Deep Transfer Learning: Application to Liver Segmentation in CT and MRI", Annual Conference on Medical Image Understanding and Analysis, UK, pp. 96-110, June, 2023.
Mousa, D., N. Zayed, and I. A. Yassine, "Correlation transfer function analysis as a biomarker for Alzheimer brain plasticity using longitudinal resting-state fMRI data.", Scientific reports, vol. 13, issue 1, pp. 21559, 2023. Abstract

Neural plasticity is the ability of the brain to alter itself functionally and structurally as a result of its experience. However, longitudinal changes in functional connectivity of the brain are still unrevealed in Alzheimer's disease (AD). This study aims to discover the significant connections (SCs) between brain regions for AD stages longitudinally using correlation transfer function (CorrTF) as a new biomarker for the disease progression. The dataset consists of: 29 normal controls (NC), and 23, 24, and 23 for early, late mild cognitive impairments (EMCI, LMCI), and ADs, respectively, along three distant visits. The brain was divided into 116 regions using the automated anatomical labeling atlas, where the intensity time series is calculated, and the CorrTF connections are extracted for each region. Finally, the standard t-test and ANOVA test were employed to investigate the SCs for each subject's visit. No SCs, along three visits, were found For NC subjects. The most SCs were mainly directed from cerebellum in case of EMCI and LMCI. Furthermore, the hippocampus connectivity increased in LMCI compared to EMCI whereas missed in AD. Additionally, the patterns of longitudinal changes among the different AD stages compared to Pearson Correlation were similar, for SMC, VC, DMN, and Cereb networks, while differed for EAN and SN networks. Our findings define how brain changes over time, which could help detect functional changes linked to each AD stage and better understand the disease behavior.

Koko, R. R. Z., I. A. Yassine, M. A. Wahed, J. K. Madete, and M. A. Rushdi, "Dynamic Construction of Outlier Detector Ensembles With Bisecting K-Means Clustering", EEE Access, vol. 11, pp. 24431-24447, 2023.
Morsy, S. E., N. Zayed, and I. A. Yassine, "Hierarchical based classification method based on fusion of Gaussian map descriptors for Alzheimer diagnosis using T-weighted magnetic resonance imaging.", Scientific reports, vol. 13, issue 1, pp. 13734, 2023. Abstract

Alzheimer's disease (AD) is considered one of the most spouting elderly diseases. In 2015, AD is reported the US's sixth cause of death. Substantially, non-invasive imaging is widely employed to provide biomarkers supporting AD screening, diagnosis, and progression. In this study, Gaussian descriptors-based features are proposed to be efficient new biomarkers using Magnetic Resonance Imaging (MRI) T-weighted images to differentiate between Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and Normal controls (NC). Several Gaussian map-based features are extracted such as Gaussian shape operator, Gaussian curvature, and mean curvature. The aforementioned features are then introduced to the Support Vector Machine (SVM). They were, first, calculated separately for the Hippocampus and Amygdala. Followed by the fusion of the features. Moreover, Fusion of the regions before feature extraction was also employed. Alzheimer's disease Neuroimaging Initiative (ADNI) dataset, formed of 45, 55, and 65 cases for AD, MCI, and NC respectively, is appointed in this study. The shape operator feature outperformed the other features, with 74.6%, and 98.9% accuracy in the case of normal vs. abnormal, and AD vs. MCI classification respectively.

Salah, P. E., M. Bibars, A. Eldeib, A. M. Ghanem, A. M. Gharib, K. Z. Abd-Elmoniem, M. A. Elattar, and I. A. Yassine, "Iterative Refinement Algorithm for Liver Segmentation Ground-Truth Generation Using Fine-Tuning Weak Labels for CT and Structural MRI", Annual Conference on Medical Image Understanding and Analysis, UK, pp. 33-47, 2023.
2022
Mousa, D., N. Zayed, and I. A. Yassine, "Alzheimer disease stages identification based on correlation transfer function system using resting-state functional magnetic resonance imaging.", PloS one, vol. 17, issue 4, pp. e0264710, 2022. Abstract

Alzheimer's disease (AD) affects the quality of life as it causes; memory loss, difficulty in thinking, learning, and performing familiar tasks. Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to investigate and analyze different brain regions for AD identification. This study investigates the effectiveness of using correlated transfer function (CorrTF) as a new biomarker to extract the essential features from rs-fMRI, along with support vector machine (SVM) ordered hierarchically, in order to distinguish between the different AD stages. Additionally, we explored the regions, showing significant changes based on the CorrTF extracted features' strength among different AD stages. First, the process was initialized by applying the preprocessing on rs-fMRI data samples to reduce noise and retain the essential information. Then, the automated anatomical labeling (AAL) atlas was employed to divide the brain into 116 regions, where the intensity time series was calculated, and the CorrTF features were extracted for each region. The proposed framework employed the SVM classifier in two different methodologies, hierarchical and flat multi-classification schemes, to differentiate between the different AD stages for early detection purposes. The ADNI rs-fMRI dataset, employed in this study, consists of 167, 102, 129, and 114 normal, early, late mild cognitive impairment (MCI), and AD subjects, respectively. The proposed schemes achieved an average accuracy of 98.2% and 95.5% for hierarchical and flat multi-classification tasks, respectively, calculated using ten folds cross-validation. Therefore, CorrTF is considered a promising biomarker for AD early-stage identification. Moreover, the significant changes in the strengths of CorrTF connections among the different AD stages can help us identify and explore the affected brain regions and their latent associations during the progression of AD.

Khder, S. M., E. A. H. Mohamed, and I. A. Yassine, "A Clustering- based Fusion System for Blastomere Localization", Biomedical Engineering: Applications, Basis and Communications, vol. 34, issue 4, pp. 2250021, 2022.
Khedr, S. M., E. A. H. Mohamed, and I. A. Yassine, "A Clustering-based Fusion System for Blastomere Localization", Biomedical Engineering: Applications, Basis and Communications, pp. 2250021, 2022.
Algumaei, A. H., R. F. Algunaid, M. A. Rushdi, and I. A. Yassine, "Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data", PlosOne, vol. 17, issue 5, pp. e0265300, 2022.
Algumaei, A. H., R. F. Algunaid, M. A. Rushdi, and I. A. Yassine, "Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data.", PloS one, vol. 17, issue 5, pp. e0265300, 2022. Abstract

Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early stages. Recently, computer-aided diagnosis techniques based on resting-state functional magnetic resonance imaging (Rs-fMRI) have been developed to tackle this challenge. In this work, we investigate different decision-level and feature-level fusion schemes for discriminating between schizophrenic and normal subjects. Four types of fMRI features are investigated, namely the regional homogeneity, voxel-mirrored homotopic connectivity, fractional amplitude of low-frequency fluctuations and amplitude of low-frequency fluctuations. Data denoising and preprocessing were first applied, followed by the feature extraction module. Four different feature selection algorithms were applied, and the best discriminative features were selected using the algorithm of feature selection via concave minimization (FSV). Support vector machine classifiers were trained and tested on the COBRE dataset formed of 70 schizophrenic subjects and 70 healthy subjects. The decision-level fusion method outperformed the single-feature-type approaches and achieved a 97.85% accuracy, a 98.33% sensitivity, a 96.83% specificity. Moreover, feature-fusion scheme resulted in a 98.57% accuracy, a 99.71% sensitivity, a 97.66% specificity, and an area under the ROC curve of 0.9984. In general, decision-level and feature-level fusion schemes boosted the performance of schizophrenia detectors based on fMRI features.

Yassine, I. A., A. M. Ghanem, N. S. Metwalli, A. Hamimi, R. Ouwerkerk, J. R. Matta, M. A. Solomon, J. M. Elin, A. M. Gharib, and K. Z. Abd-Elmoniem, "Native-resolution myocardial principal Eulerian strain mapping using convolutional neural networks and Tagged Magnetic Resonance Imaging", Computers in biology and medicine, vol. 141, pp. 105041, 2022.
Yassine, I. A., A. M. Ghanem, N. S. Metwalli, A. Hamimi, R. Ouwerkerk, J. R. Matta, M. A. Solomon, J. M. Elinoff, A. M. Gharib, and K. Z. Abd-Elmoniem, "Native-resolution myocardial principal Eulerian strain mapping using convolutional neural networks and Tagged Magnetic Resonance Imaging.", Computers in biology and medicine, vol. 141, pp. 105041, 2022. Abstract

BACKGROUND: Assessment of regional myocardial function at native pixel-level resolution can play a crucial role in recognizing the early signs of the decline in regional myocardial function. Extensive data processing in existing techniques limits the effective resolution and accuracy of the generated strain maps. The purpose of this study is to compute myocardial principal strain maps ε and ε from tagged MRI (tMRI) at the native image resolution using deep-learning local patch convolutional neural network (CNN) models (DeepStrain).

METHODS: For network training, validation, and testing, realistic tMRI datasets were generated and consisted of 53,606 cine images simulating the heart, the liver, blood pool, and backgrounds, including ranges of shapes, positions, motion patterns, noise, and strain. In addition, 102 in-vivo image datasets from three healthy subjects, and three Pulmonary Arterial Hypertension patients, were acquired and used to assess the network's in-vivo performance. Four convolutional neural networks were trained for mapping input tagging patterns to corresponding ground-truth principal strains using different cost functions. Strain maps using harmonic phase analysis (HARP) were obtained with various spectral filtering settings for comparison. CNN and HARP strain maps were compared at the pixel level versus the ground-truth and versus the least-loss in-vivo maps using Pearson correlation coefficients (R) and the median error and Inter-Quartile Range (IQR) histograms.

RESULTS: CNN-based local patch DeepStrain maps at a phantom resolution of 1.1mm × 1.1 mm and in-vivo resolution of 2.1mm × 1.6 mm were artifact-free with multiple fold improvement with ε ground-truth median error of 0.009(0.007) vs. 0.32(0.385) using HARP and ε ground-truth error of 0.016(0.021) vs. 0.181(0.08) using HARP. CNN-based strain maps showed substantially higher agreement with the ground-truth maps with correlation coefficients R > 0.91 for ε and ε compared to R < 0.21 and R < 0.82 for HARP-generated maps, respectively.

CONCLUSION: CNN-generated Eulerian strain mapping permits artifact-free visualization of myocardial function at the native image resolution.

2021
Abd‐Elmoniem, K. Z., I. A. Yassine, N. S. Metwalli, A. Hamimi, R. Ouwerkerk, J. R. Matta, M. Wessel, M. A. Solomon, J. M. Elinoff, A. M. Ghanem, et al., "Direct pixel to pixel principal strain mapping from tagging MRI using end to end deep convolutional neural network (DeepStrain)", Scientific Reports, vol. 11, pp. 20321, 2021.
Abd-Elmoniem, K. Z., I. A. Yassine, N. S. Metwalli, A. Hamimi, R. Ouwerkerk, J. R. Matta, M. Wessel, M. A. Solomon, J. M. Elinoff, A. M. Ghanem, et al., "Direct pixel to pixel principal strain mapping from tagging MRI using end to end deep convolutional neural network (DeepStrain)", Scientific Reports, vol. 11, issue 1, pp. 23021, 2021.
2020
Marzaban, E. N., A. M. Eldeib, I. A. Yassine, and Y. M. Kadah, "Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks", PLOSONE, vol. 15, issue 3, pp. e0230409, 2020.
2018
Eldeeb, G., N. Zayed, and I. A. Yassine, "Alzheimer’s Disease Classification Using Bag-of-Words Based on Visual Pattern of Diffusion Anisotropy for DTI Imaging", 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, Hawaii, pp. 57-60, 2018.
Yassine, I. A., W. M. Eldeib, K. A. Gad, Y. A. Ashour, I. A. Yassine, and A. O. Hosy, "Cognitive functions, electroencephalographic and diffusion tensor imaging changes in children with active idiopathic epilepsy", Epilepsy & Behavior, vol. 84, pp. 135-141, 2018.
Algunaid, R. F., A. H. Algumaei, M. A. Rushdi, and I. A. Yassine, "Schizophrenic patient identification using graph-theoretic features of resting-state fMRI data", Biomedical Signal Processing and Control , vol. 43, pp. 289–299, 2018.
Esmail, E. H., Hadeel M. Seif El Dein, I. A. Yassine, and R. Zakaria, "Thalamocortical Tracts, but not the Putamen, Present Microstructural Abnormalities in Juvenile Myoclonic Epilepsy: A Diffusion Tractography Study", Journal of Pediatric Epilepsy, 2018.
2017
Elmahdy, M. S., S. S. Abdeldayem, and I. A. Yassine, "Low quality dermal image classification using transfer learning", IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) , pp. 373-376, 2017.
2016
Khalaf, A. F., I. A. Yassine, and A. S. Fahmy, "Convolutional neural networks for deep feature learning in retinal vessel segmentation ", IEEE International Conference on Image Processing (ICIP), Phoenix, USA, Sept, 2016.
Abduallatif, N. A., Sara G Elsherbini, B. S. Boshra, and I. A. A. Yassine, "Brain-Computer Interface controlled functional electrical stimulation system for paralyzed arm", 8th Cairo International Biomedical Engineering Conference (CIBEC), , Cairo, Egypt, pp. 48-51, 2016.
Mousa, D., N. Zayed, and I. A. Yassine, "Factors Affecting the Level Set Segmentation of the Heart Ventricles in Short Axis Cardiac Perfusion MRI Images", The 39th Conference of The Canadian Medical and Biological Engineering/La Societe Canadiénné de Génie Biomédical, 2016.
Mousa, D., N. M. Zayed, and I. A. Yassine, "Factors Affecting the Segmentation of the Heart Ventricles in Short Axis Cardiac Perfusion MRI Images", INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY , vol. 15, issue 11, pp. 7218-7226, 2016.