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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 271–280 of 436 papers

TitleStatusHype
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation—0
A Multiscale Patch Based Convolutional Network for Brain Tumor Segmentation—0
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation—0
A Multi-View Dynamic Fusion Framework: How to Improve the Multimodal Brain Tumor Segmentation from Multi-Views?—0
Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI—0
Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities—0
Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities—0
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation—0
An Ensemble Approach for Brain Tumor Segmentation and Synthesis—0
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