Papers by Mohamed Abou El-Ghar
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Urology, Jul 1, 2013
Egyptian Journal of Radiology and Nuclear Medicine
Background Bosniak classification improves sensitivity and specificity for malignancy among cysti... more Background Bosniak classification improves sensitivity and specificity for malignancy among cystic renal masses characterized with MRI. The quantitative parameters derived from diffusion-weighted imaging, and contrast enhancement, can be used in distinguishing between benign and malignant cystic renal masses. Methods This prospective observational study included 58 patients (39 male and 19 female) with complicated cystic renal mass initially diagnosed by US or CT. All patients underwent multiparametric MRI study (Pre- and Post-Gd-enhanced T1WI, T2WI and DWI) by using 3 Tesla MRI scanner. Each cystic renal lesion was assigned a category based on Bosniak classification. Demographic data were recorded. ADC ratio, dynamic enhancement parameters in both corticomedullary and nephrographic phases as well as absolute washout were calculated and compared using ROC curve analysis. Results The sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy ...
Role of artificial intelligence and radiomics in diagnosing renal tumors: a survey
Elsevier eBooks, 2023
Biomedicines
The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has great potential ... more The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has great potential in the diagnosis, therapy, and follow-up of patients with chronic kidney disease (CKD). Towards that end, precise kidney segmentation from DCE-MRI data becomes a prerequisite processing step. Exploiting the useful information about the kidney’s shape in this step mandates a registration operation beforehand to relate the shape model coordinates to those of the image to be segmented. Imprecise alignment of the shape model induces errors in the segmentation results. In this paper, we propose a new variational formulation to jointly segment and register DCE-MRI kidney images based on fuzzy c-means clustering embedded within a level-set (LSet) method. The image pixels’ fuzzy memberships and the spatial registration parameters are simultaneously updated in each evolution step to direct the LSet contour toward the target kidney. Results on real medical datasets of 45 subjects demonstrate the s...
Egyptian Journal of Radiology and Nuclear Medicine
Background Emphysematous pyelonephritis (EPN) is one of the most serious urologic emergency which... more Background Emphysematous pyelonephritis (EPN) is one of the most serious urologic emergency which should be diagnosed and treated adequately to prevent impending septic shock and death. Computed tomography (CT) is the gold standard radiologic modality for diagnosis, grading and predicting the outcome. We aimed in this study to define the initial CT radiological findings correlated with EPN conservative management success. Results This study involved 54 patients (42 women and 12 males) with a mean age of 48 ± 10 years. EPN grades I, II, III, and IV were noticed in 12, 17, 20, and 5 patients, respectively. Ten patients (18.5%) received successful conservative management. On the other hand, renal drainage was needed in 42 patients (77.8%). Delayed nephrectomy was required in two cases (3.7%). In univariate and multivariate analyses, the absence of hydronephrosis and decreased air locules volume were predictors of conservative treatment success (P = 0.003 and 0.01, respectively). Conclu...
Bioengineering
The segmentation of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) of the kidney i... more The segmentation of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) of the kidney is a fundamental step in the early and noninvasive detection of acute renal allograft rejection. In this paper, a new and accurate DCE-MRI kidney segmentation method is proposed. In this method, fuzzy c-means (FCM) clustering is embedded into a level set method, with the fuzzy memberships being iteratively updated during the level set contour evolution. Moreover, population-based shape (PB-shape) and subject-specific shape (SS-shape) statistics are both exploited. The PB-shape model is trained offline from ground-truth kidney segmentations of various subjects, whereas the SS-shape model is trained on the fly using the segmentation results that are obtained for a specific subject. The proposed method was evaluated on the real medical datasets of 45 subjects and reports a Dice similarity coefficient (DSC) of 0.953 ± 0.018, an intersection-over-union (IoU) of 0.91 ± 0.033, and 1.10 ± 1.4 in ...
Scientific Reports
Early diagnosis of transplanted kidney function requires precise Kidney segmentation from Dynamic... more Early diagnosis of transplanted kidney function requires precise Kidney segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging images as a preliminary step. In this regard, this paper aims to propose an automated and accurate DCE-MRI kidney segmentation method integrating fuzzy c-means (FCM) clustering and Markov random field modeling into a level set formulation. The fuzzy memberships, kidney’s shape prior model, and spatial interactions modeled using a second-order MRF guide the LS contour evolution towards the target kidney. Several experiments on real medical data of 45 subjects have shown that the proposed method can achieve high and consistent segmentation accuracy regardless of where the LS contour was initialized. It achieves an accuracy of 0.956 ± 0.019 in Dice similarity coefficient (DSC) and 1.15 ± 1.46 in 95% percentile of Hausdorff distance (HD95). Our quantitative comparisons confirm the superiority of the proposed method over several LS methods with an...
Fuzzy Membership-Driven Level Set for Automatic Kidney Segmentation from DCE-MRI
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Kidney segmentation from Dynamic Contrast Enhanced Magnetic Resonance Images (DCE-MRI) is an impo... more Kidney segmentation from Dynamic Contrast Enhanced Magnetic Resonance Images (DCE-MRI) is an important step for the early detection of transplanted kidney rejection. In this paper, an accurate kidney segmentation method from DCE-MRI is proposed. In the proposed method, fuzzy c-means (FCM) algorithm is combined with a geometric deformable model (level set) method to accurately extract the kidney from its background. The FCM algorithm is applied to the input image and the obtained result is used as the initial contour for the level set method. The evolution of the level set boundary is controlled using the kidney shape prior model and the memberships of the pixels computed using the FCM algorithm. The proposed method has been tested on 40 subjects, and experimental results confirm the efficiency, reliability, and accuracy of the proposed method.
Egyptian Journal of Radiology and Nuclear Medicine
Background Zinner's syndrome is a mesonephric duct anomaly characterized by unilateral renal ... more Background Zinner's syndrome is a mesonephric duct anomaly characterized by unilateral renal agenesis, ipsilateral seminal vesicle cyst, and ipsilateral ejaculatory duct obstruction due to insult occurred at urogenital tract embryogenesis during the first trimester. In the third and fourth decades of life, it is frequently diagnosed when patients begin to be symptomatic, such as lower urinary tract symptoms, infertility and painful ejaculation. Case presentation Herein we illustrate case review including five patients diagnosed as Zinner’s syndrome, three of them complaining from infertility; however, the remaining two cases were fertile and incidentally diagnosed. Conclusions Radiological investigations play significant role in the diagnostic and management processes including US, CT and MRI for detecting ipsilateral renal agenesis and unilateral seminal vesicles dilatation, but evaluation of ejaculatory duct can be done only by MRI and transrectal ultrasound; however, the latt...
Egyptian Journal of Radiology and Nuclear Medicine
Background Prostatic cysts are uncommon, typically asymptomatic and discovered by chance during i... more Background Prostatic cysts are uncommon, typically asymptomatic and discovered by chance during imaging. Prostatic cysts in the midline are less prevalent and primarily seen in the posterior aspect of the prostate. Case presentation We describe a case of a 32-year-old man with a complaint of left loin pain and a little sensation of pelvic discomfort. Ultrasound was done revealing small pelvic cystic structure related to urinary bladder base and neck with possibility to be prostatic in origin. Transrectal ultrasound and pelvic magnetic resonance imaging were done and showed an anteriorly located midline prostatic cyst protruding into the bladder lumen, with no communication with the urethra on conventional ascending urethrogram. Conclusion The rare relationship between the cyst, bladder neck, and prostate make this case to some extent unique and further interesting. To our best knowledge, this is the eighth documented case in the literature to describe an anteriorly located midline i...
Journal of Urology
and under general anesthesia. The incidence of UTI during the first 30 POD was higher in group A ... more and under general anesthesia. The incidence of UTI during the first 30 POD was higher in group A than group B (4.6% vs 2%), however it was not statistically significant (p[.25). Lower urinary symptoms in the first 30 POD also presented more frequently in group A (6%) than group B (0%) with p[0.08. Only 4% of group A required a redo-procedure; till the date none of the patients of group B had required a redopyeloplasty. CONCLUSIONS: Pediatric pyeloplasty without foley placement showed a favorable safety profile with durable post-operative outcomes across a wide range of clinical variables. "Foley-less" pyeloplasty had benefits beyond a low incidence of UTIs, including shorter operative time and days from discharge, and increasing the frequency of ureteral stents left on a string, negating the need for cystoscopic stent removal under general anesthesia.
Stone Composition Independently Predicts Post-Operative Outcomes After Percutaneous Nephrolithotomy (PCNL)
Sensors, 2022
Prostate cancer, which is also known as prostatic adenocarcinoma, is an unconstrained growth of e... more Prostate cancer, which is also known as prostatic adenocarcinoma, is an unconstrained growth of epithelial cells in the prostate and has become one of the leading causes of cancer-related death worldwide. The survival of patients with prostate cancer relies on detection at an early, treatable stage. In this paper, we introduce a new comprehensive framework to precisely differentiate between malignant and benign prostate cancer. This framework proposes a noninvasive computer-aided diagnosis system that integrates two imaging modalities of MR (diffusion-weighted (DW) and T2-weighted (T2W)). For the first time, it utilizes the combination of functional features represented by apparent diffusion coefficient (ADC) maps estimated from DW-MRI for the whole prostate in combination with texture features with its first- and second-order representations, extracted from T2W-MRIs of the whole prostate, and shape features represented by spherical harmonics constructed for the lesion inside the pr...
AbstractBackground: Prostate cancer is a worldwide commonneoplastic disease in males. Loco-region... more AbstractBackground: Prostate cancer is a worldwide commonneoplastic disease in males. Loco-regional Staging of patho-logically proved Prostate cancer is of great impact on man-agement planning. Multi-parametric MRI is a novel highlysensitive modality in the evaluation of local behavior andaggressiveness of prostatic tumors.Aim of Study: This study aimed to assess the ability ofMult-Parmateric MRI in assessing the loco-regional featuresand aggressiveness of the proved prostatic cancer and howthat will impact the therapeutic individual planes.Methods: We evaluated 40 male patients (mean age 69years, range 56-87 years) with pathologically proved prostatecancer. They underwent a Multi-parametric MRI examinationat (1.5T Toshiba - USA) at Aswan University Hospital after at least 6 weeks after TRUS biopsy. MRI protocol included T2WI, diffusion-weighted MR and Dynamic Contrast (DCE). Each examination was analyzed to asses the diagnostic performance of multi-parametric MRI in the assessment ...
Big Data in Prostate Cancer
Big Data in Multimodal Medical Imaging, 2019
A New Fast Framework for Early Detection of Prostate Cancer Without Prostate Segmentation
2018 IEEE International Conference on Imaging Systems and Techniques (IST), 2018
This paper presents a computer-aided diagnosis (CAD) system for early detection of prostate cance... more This paper presents a computer-aided diagnosis (CAD) system for early detection of prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) acquired at six different b-values. Our system starts by defining a region of interest (ROI) that includes the prostate across the different slices of the input DWI volume. Then, the apparent diffusion coefficient (ADC) of the defined ROI is calculated, normalized and refined. Then, the probability density functions (PDFs) of the refined ADC volumes at the distinct b-values are constructed. Finally, the classification of prostate into either benign or malignant is achieved using a classification system of two stages. The proposed system is the first system of its type that has the ability to detect prostate cancer without any prior processing (e.g., the segmentation of the prostate region). Evaluation of the proposed system is done using DWI datasets acquired from 45 patients (20 benign and 25 malignant) at six distinct b-values....
Sensors, 2021
Prostate cancer is one of the most identified cancers and second most prevalent among cancer-rela... more Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histo...
A DCE-MRI-Based Noninvasive CAD System for Prostate Cancer Diagnosis
Prostate Cancer Imaging, 2018
A 3D CNN with a Learnable Adaptive Shape Prior for Accurate Segmentation of Bladder Wall Using MR Images
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020
A 3D deep learning-based convolution neural network (CNN) is developed for accurate segmentation ... more A 3D deep learning-based convolution neural network (CNN) is developed for accurate segmentation of pathological bladder (both wall border and pathology) using T2-weighted magnetic resonance imaging (T2W-MRI). Our system starts with a preprocessing step for data normalization to a unique space and extraction of a region-of-interest (ROI). The major stage utilizes a 3D CNN for pathological bladder segmentation, which contains a network, called CNN1, that aims to segment the bladder wall (BW) with pathology. However, due to the similar visual appearance of BW and pathology, the CNN1 can not separate them. Thus, we developed another network (CNN2) with an additional pathway to extract BW only. The second pathway in CNN2 is fed with a 3D learnable adaptive shape prior model. To remove noisy and scattered predictions, the networks' soft outputs are refined using a fully connected conditional random field. Our framework achieved accurate segmentation results for the BW and tumor as documented by the Dice similarity coefficient and Hausdorff distance. Moreover, comparative results against the other segmentation approach documented the superiority of our framework to provide accurate results for pathological BW segmentation.
A Deep Learning-Based Approach for Accurate Segmentation of Bladder Wall using MR Images
2019 IEEE International Conference on Imaging Systems and Techniques (IST), 2019
In this paper, a deep learning-based convolution neural network (CNN) is developed for accurate s... more In this paper, a deep learning-based convolution neural network (CNN) is developed for accurate segmentation of the bladder wall using T2-weighted magnetic resonance imaging (T2W-MRI). Our framework utilizes a dual pathway, two-dimensional CNN for pathological bladder segmentation. Due to large bladder shape variability across subjects and the existence of pathology, a learnable adaptive shape prior (ASP) model is incorporated into our framework. To obtain the goal regions, the neural network fuses the MR image data for the first pathway, and the estimated ASP model for the second pathway. To remove noisy and scattered predictions, the CNN soft output is refined using a fully connected conditional random field (CRF). Our pipeline has been tested and evaluated using a leave-one-subject-out approach (LOSO) on twenty MRI data sets. Our framework achieved accurate segmentation results for the bladder wall and tumor as documented by the Dice similarity coefficient (DSC) and Hausdorff distance (HD). Moreover, comparative results against other segmentation approaches documented the superiority of our framework to provide accurate results for pathological bladder wall segmentation.
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Papers by Mohamed Abou El-Ghar