A5054018291- Renal cell carcinoma treatment
- MRI in cancer diagnosis
- Pediatric Urology and Nephrology Studies
- Renal and related cancers
- Indoor and Outdoor Localization Technologies
- Radiomics and Machine Learning in Medical Imaging
- AI in cancer detection
- Advanced Neural Network Applications
- Brain Tumor Detection and Classification
- Colorectal Cancer Screening and Detection
- Water Quality Monitoring Technologies
- Underwater Vehicles and Communication Systems
- Medical Image Segmentation Techniques
University of Guelph
2022-2024
PurposeAccurate detection of small renal masses (SRM) is a fundamental step for automated classification benign and malignant or indolent aggressive tumors. Magnetic resonance image (MRI) may outperform computed tomography (CT) SRM subtype differentiation due to improved tissue characterization, but less explored compared CT. The objective this study autonomously detect on contrast-enhanced magnetic images (CE-MRI).ApproachIn paper, we described novel, fully methodology accurate localization...
Purpose: Multiparametric magnetic resonance imaging (mp-MRI) is being investigated for kidney cancer because of better soft tissue contrast ability. The necessity manual labels makes the development supervised segmentation algorithms challenging each mp-MRI protocol. Here, we developed a transfer learning-based approach to improve on small dataset five other sequences. Approach: We proposed fully automated two-dimensional (2D) attention U-Net model T1 weighted-nephrographic phase enhanced...
Magnetic resonance imaging (MRI) is well suited for Solid renal masses (SRMs) characterization (e.g., benign vs. malignant) due to its superior soft tissue contrast. Though mass detection and using deep-learning (DL) methods have been extensively studied computed tomography (CT) images, those same tasks are yet be investigated on MRI images. SRMs need active surveillance as they consist of biologically diverse heterogeneous groups or malignant masses. Among them, clear cell carcinoma (ccRCC)...
Due to superior soft tissue contrast afforded by magnetic resonance imaging (MRI), there is great potential for multi-parametric MRI (mpMRI) the detection and eventual classification of renal masses (RMs). In this study, we investigated fully automated deep learning methods RMs using T2-Weighted (T2W) spin-echo two contrast-enhanced T1-Weighted gradient-echo-corticomedullary (T1W-CM), nephrographic-phase (T1W-NG), In-phase (T1W-IP) opposed-phase (T1W-OP) images. The dataset contained mpMRI...
Due to the superior soft tissue contrast in magnetic resonance imaging (MRI), MRI may be well suited for renal mass characterization (e.g., benign vs. malignant). Though detection and using deeplearning (DL) methods have been extensively studied CT images, those same tasks are yet investigated on MR images. Existing algorithms require manual segmentation, therefore development of localize detect masses is important fully automatically. In this study, we developed a DL-based automated model...
Multi-parametric magnetic resonance imaging (mp-MRI) is a promising tool for diagnosis of renal masses and may outperform computed tomography (CT) to differentiate between benign malignant due superior soft tissue contrast. Deep learning (DL)-based methods kidney segmentation are under-explored in mp-MRI which consists several pulse sequences, including primarily T2-weighted (T2W) contrast-enhanced (CE) images. MRI images have domain shift differences acquisition systems image protocols,...