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Found 24 papers, 12 papers with code
A Lightweight Causal Model for Interpretable Subject-level Prediction
Recent years have seen a growing interest in methods for predicting a variable of interest, such as a subject's diagnosis, from medical images.
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI
Clinical monitoring of metastatic disease to the brain can be a laborious and time-consuming process, especially in cases involving multiple metastases when the assessment is performed manually.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)
Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting
The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023.
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma
Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality.
An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation
In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans.
Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features
In this paper we propose a method for predicting the status of MGMT promoter methylation in high-grade gliomas.
Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images
Survival prediction models can potentially be used to guide treatment of glioblastoma patients.
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution.
An augmentation strategy to mimic multi-scanner variability in MRI
Most publicly available brain MRI datasets are very homogeneous in terms of scanner and protocols, and it is difficult for models that learn from such data to generalize to multi-center and multi-scanner data.
3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology
NTNC traditionally requires a volumetric MRI scan, acquired either ex vivo or a short time prior to death.
A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis.
A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients.
A Learning Strategy for Contrast-agnostic MRI Segmentation
These samples are produced using the generative model of the classical Bayesian segmentation framework, with randomly sampled parameters for appearance, deformation, noise, and bias field.
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Relevance Vector Machines for harmonization of MRI brain volumes using image descriptors
In this work, we explore a novel approach to harmonize brain volume measurements by using only image descriptors.
Semi-Supervised Variational Autoencoder for Survival Prediction
In this paper we propose a semi-supervised variational autoencoder for classification of overall survival groups from tumor segmentation masks.
Joint inference on structural and diffusion MRI for sequence-adaptive Bayesian segmentation of thalamic nuclei with probabilistic atlases
Segmentation of structural and diffusion MRI (sMRI/dMRI) is usually performed independently in neuroimaging pipelines.
PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation
In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input acquisition.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning
In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas.
Personalized Radiotherapy Design for Glioblastoma Using Mathematical Models, Multimodal Scans and Bayesian Inference
Here we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans.
Computational Engineering, Finance, and Science
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation.
