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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 291–300 of 436 papers

TitleStatusHype
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation—0
RFNet: Region-Aware Fusion Network for Incomplete Multi-Modal Brain Tumor SegmentationCode1
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task—0
MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architecturesCode1
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors—0
A Multi-View Dynamic Fusion Framework: How to Improve the Multimodal Brain Tumor Segmentation from Multi-Views?—0
HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation—0
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty—0
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation—0
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