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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 311–320 of 436 papers

TitleStatusHype
Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation—0
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task—0
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation—0
Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function—0
HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation—0
HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging—0
Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation—0
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation—0
Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction—0
Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior—0
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