44 citations · 214 across the 20 of their papers we have counts for
21 papers
WebMRIQC: A Web-Based Implementation of MRIQC for Accessible MRI Image Quality Assessment in Resource-Constrained Settings
Philip Nkwam, Ifeoluwa Oladeji, Sekinat Zurakat-Aderibigbe +9
Reliable quality control (QC) of magnetic resonance imaging (MRI) is essential for reliable diagnostic neuroimaging, yet standard manual assessment is subjective and time-consuming…
From Manuals to Maintenance: Fine-Tuning MedGemma for Multi-Modal Imaging System Support in Low-Resource Settings
Bernes Lorier Atabonfack, Zion Kongbi Nfo, Ahmed Tahiru Issah +9
Imaging device downtime is a major barrier to healthcare delivery in low- and middle-income countries (LMICs), often driven by limited access to specialized biomedical engineering…
ULF-Synth: Physics-Guided Ultra-Low-Field MRI Enhancement for Pediatric Neuroimaging
Toufiq Musah, Salvatore Calcagno, Federica Proietto Salanitri +3
Ultra-low-field (ULF) MRI offers portable and accessible neuroimaging but suffers from reduced signal-to-noise ratio and limited spatial resolution compared to high-field (HF) syst…
Empowering Medical Equipment Sustainability in Low-Resource Settings: An AI-Powered Diagnostic and Support Platform for Biomedical Technicians
Bernes Lorier Atabonfack, Ahmed Tahiru Issah, Mohammed Hardi Abdul Baaki +5
In low- and middle-income countries (LMICs), a significant proportion of medical diagnostic equipment remains underutilized or non-functional due to a lack of timely maintenance, l…
Domain-Adaptive Transformer for Data-Efficient Glioma Segmentation in Sub-Saharan MRI
Ilerioluwakiiye Abolade, Aniekan Udo, Augustine Ojo +5
Glioma segmentation is critical for diagnosis and treatment planning, yet remains challenging in Sub-Saharan Africa due to limited MRI infrastructure and heterogeneous acquisition…
Towards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation
Toufiq Musah, Chinasa Kalaiwo, Maimoona Akram +7
Automated segmentation of BUS images is important for precise lesion delineation and tumor characterization, but is challenged by inherent artifacts and dataset inconsistencies. In…
Themed Challenges to Solve Data Scarcity in Africa: A Proposition for Increasing Local Data Collection and Integration
Mubaraq Yakubu, Udunna Anazodo, Maruf Adewole +6
In Africa, the scarcity of computational resources and medical datasets remains a major hurdle to the development and deployment of artificial intelligence (AI) tools in clinical s…
Brain Tumor Segmentation in Sub-Sahara Africa with Advanced Transformer and ConvNet Methods: Fine-Tuning, Data Mixing and Ensembling
Toufiq Musah, Chantelle Amoako-Atta, John Amankwaah Otu +21
Brain tumors are among the deadliest cancers worldwide, with particularly devastating impact in Sub-Saharan Africa (SSA) where limited access to medical imaging infrastructure and…
BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
Florian Kofler, Marcel Rosier, Mehdi Astaraki +34
The Brain Tumor Segmentation (BraTS) cluster of challenges has significantly advanced brain tumor image analysis by providing large, curated datasets and addressing clinically rele…
Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI
Nazanin Maleki, Raisa Amiruddin, Ahmed W. Moawad +240
Despite continuous advancements in cancer treatment, brain metastatic disease remains a significant complication of primary cancer and is associated with an unfavorable prognosis.…
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023
Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu +77
Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%.…
The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI
Maria Correia de Verdier, Rachit Saluja, Louis Gagnon +82
Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the gene…
Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge
Dominic LaBella, Valeriia Abramova, Mehdi Astaraki +102
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aimed to advance automated segmentation algorithms using the largest known multi-institutional da…
Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
Dominic LaBella, Ujjwal Baid, Omaditya Khanna +119
We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in…
The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu +68
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less…
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI
Ahmed W. Moawad, Anastasia Janas, Ujjwal Baid +229
The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS…
The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)
Maruf Adewole, Jeffrey D. Rudie, Anu Gbadamosi +48
Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 ye…
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma
Dominic LaBella, Maruf Adewole, Michelle Alonso-Basanta +57
Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists,…
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu +70
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less…
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
Hongwei Bran Li, Gian Marco Conte, Qingqiao Hu +62
Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input m…
The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting
Florian Kofler, Felix Meissen, Felix Steinbauer +103
A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition ti…