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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 321–330 of 436 papers

TitleStatusHype
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation—0
Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features—0
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images—0
Brain tumor segmentation with missing modalities via latent multi-source correlation representation—0
Brain Tumor Survival Prediction using Radiomics Features—0
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans—0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023—0
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet—0
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection—0
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