Brain Tumor Segmentation
127 papers with code • 9 benchmarks • 4 datasets
Brain Tumor Segmentation is a medical image analysis task that involves the separation of brain tumors from normal brain tissue in magnetic resonance imaging (MRI) scans. The goal of brain tumor segmentation is to produce a binary or multi-class segmentation map that accurately reflects the location and extent of the tumor.
( Image credit: Brain Tumor Segmentation with Deep Neural Networks )
Benchmarks
These leaderboards are used to track progress in Brain Tumor Segmentation
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Libraries
Use these libraries to find Brain Tumor Segmentation models and implementations
BraTS 2017
BraTS 2015
BraTS 2013
BraTS 2014
Latest papers with no code
Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset
Glioblastoma is a highly aggressive and lethal form of brain cancer.
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation
In our evaluation, we compared this improved model to two benchmarks: the pretrained SAM and the widely used model, nnUNetv2.
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks
Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes.
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology
Medical imaging plays a critical role in the diagnosis and treatment planning of various medical conditions, with radiology and pathology heavily reliant on precise image segmentation.
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation
An image-to-image translation strategy between imaging modalities is used to produce annotated pseudo-target volumes and improve generalization to the unannotated target modality.
Segment Anything Model for Brain Tumor Segmentation
Glioma is a prevalent brain tumor that poses a significant health risk to individuals.
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars
However, the adoption of foundational models in the medical domain presents a challenge due to the difficulty and expense of labeling sufficient data for adaptation within hospital systems.
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data
Brain tumors, particularly glioblastoma, continue to challenge medical diagnostics and treatments globally.
Automated ensemble method for pediatric brain tumor segmentation
Brain tumors remain a critical global health challenge, necessitating advancements in diagnostic techniques and treatment methodologies.
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation
To address these challenges, we present a differential privacy (DP) federated deep learning framework in medical image segmentation.
