Maruf Adewole
A5083450354- Radiomics and Machine Learning in Medical Imaging
- Brain Tumor Detection and Classification
- Glioma Diagnosis and Treatment
- Medical Imaging and Analysis
- Meningioma and schwannoma management
- Advanced Neural Network Applications
- AI in cancer detection
- COVID-19 diagnosis using AI
- Medical Image Segmentation Techniques
- Brain Metastases and Treatment
- Radiation Therapy and Dosimetry
- Digital Imaging for Blood Diseases
- Artificial Intelligence in Healthcare and Education
- Generative Adversarial Networks and Image Synthesis
- Chemical Reactions and Isotopes
- Advanced Radiotherapy Techniques
- Radiation Dose and Imaging
- Medical Imaging Techniques and Applications
- Traumatic Brain Injury and Neurovascular Disturbances
- Advanced MRI Techniques and Applications
University of Lagos
2022-2025
Pinnacle Clinical Research
2024
George Washington University
2024
National Institutes of Health
2024
Children's Hospital of Philadelphia
2024
University of California, Irvine
2024
Indiana University – Purdue University Indianapolis
2024
Lagos University Teaching Hospital
2022
McGill University
2022
Hospital of the University of Pennsylvania
2022
We describe the design and results from BraTS 2023 Intracranial Meningioma Segmentation Challenge. The Challenge differed prior Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic anatomical presentation a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data largest multi-institutional systematically expert annotated multilabel multi-sequence...
Clinical monitoring of metastatic disease to the brain can be a laborious and time-consuming process, especially in cases involving multiple metastases when assessment is performed manually. The Response Assessment Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes unidimensional longest diameter, commonly used clinical research settings evaluate response therapy patients with metastases. However, accurate volumetric lesion surrounding peri-lesional edema holds significant...
Pediatric tumors of the central nervous system are most common cause cancer-related death in children. The five-year survival rate for high-grade gliomas children is less than 20\%. Due to their rarity, diagnosis these entities often delayed, treatment mainly based on historic concepts, and clinical trials require multi-institutional collaborations. MICCAI Brain Tumor Segmentation (BraTS) Challenge a landmark community benchmark event with successful history 12 years resource creation...
The Radiological Society of North America (RSNA) and the Medical Image Computing Computer Assisted Intervention (MICCAI) have led a series joint panels seminars focused on present impact future directions artificial intelligence (AI) in radiology. These conversations collected viewpoints from multidisciplinary experts radiology, medical imaging, machine learning current clinical penetration AI technology radiology how it is impacted by trust, reproducibility, explainability, accountability....
“Just Accepted” papers have undergone full peer review and been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, proof before it is published its final version. Please note that during production of the copyedited article, errors may be discovered which could affect content. The BraTS-Africa Dataset first annotated publicly available brain imaging dataset from an African population. It contains three-dimensional MRI scans,...
Gliomas are the most common type of primary brain tumors. Although gliomas relatively rare, they among deadliest types cancer, with a survival rate less than 2 years after diagnosis. challenging to diagnose, hard treat and inherently resistant conventional therapy. Years extensive research improve diagnosis treatment have decreased mortality rates across Global North, while chances individuals in low- middle-income countries (LMICs) remain unchanged significantly worse Sub-Saharan Africa...
Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity mortality. Radiologists, neurosurgeons, neuro-oncologists, radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, longitudinal monitoring; yet automated, objective, quantitative tools non-invasive assessment of meningiomas mpMRI lacking. The BraTS meningioma 2023 challenge will provide a community standard benchmark state-of-the-art...
We describe the design and results from BraTS 2023 Intracranial Meningioma Segmentation Challenge. The Challenge differed prior Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic anatomical presentation a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data largest multi-institutional systematically expert annotated multilabel multi-sequence...
Pediatric tumors of the central nervous system are most common cause cancer-related death in children. The five-year survival rate for high-grade gliomas children is less than 20%. Due to their rarity, diagnosis these entities often delayed, treatment mainly based on historic concepts, and clinical trials require multi-institutional collaborations. Here we present CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused pediatric brain with data acquired across multiple international...
Gliomas are the most common malignant primary brain tumors in adults and one of deadliest types cancer. There many challenges treatment monitoring due to genetic diversity high intrinsic heterogeneity appearance, shape, histology, response. Treatments include surgery, radiation, systemic therapies, with magnetic resonance imaging (MRI) playing a key role planning post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on glioma MRI will provide community...
Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma children is less than 20%. development new treatments dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present results BraTS-PEDs 2023 challenge, first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This utilized data acquired...
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs with expert-annotated target labels for patients intact or post-operative meningioma that underwent either conventional external beam stereotactic radiosurgery. Each case includes a defaced 3D post-contrast T1-weighted MRI in its native acquisition space, accompanied by...
A myriad of algorithms for the automatic analysis brain MR images is available to support clinicians in their decision-making. For tumor patients, image acquisition time series typically starts with a scan that already pathological. This poses problems, as many are designed analyze healthy brains and provide no guarantees featuring lesions. Examples include but not limited anatomy parcellation, tissue segmentation, extraction. To solve this dilemma, we introduce BraTS 2023 inpainting...
Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with without contrast enhancement, T2-weighted images, FLAIR images. However, some sequences are often missing in practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability substitute modalities gain is highly...
Abstract Magnetic Resonance Imaging (MRI) employs the use of magnetic field and radio waves to produce images body. Quality Control (QC) is essential for ensuring optimal performance MRI systems, as recommended by American College Radiology (ACR), Association Physicists in Medicine (AAPM), International Society (ISMRM). This survey examines status systems QC Nigeria. Questionnaires were administered through google form Radiologists, Radiographers, Medical Physicists, biomedical engineers...