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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 241–250 of 436 papers

TitleStatusHype
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks—0
Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement—0
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism—0
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty—0
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors—0
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation—0
Unsupervised Brain Tumor Segmentation with Image-based Prompts—0
Consistent estimation of the max-flow problem: Towards unsupervised image segmentation—0
Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting—0
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