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Brain Tumor Segmentation

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 )

Papers

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Showing 191–200 of 436 papers

TitleStatusHype
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities—0
Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation—0
Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection—0
A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation—0
Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation—0
A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images—0
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology—0
Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI—0
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint—0
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