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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 171–180 of 436 papers

TitleStatusHype
Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?—0
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation—0
Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities—0
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology—0
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models—0
Deep Learning with Mixed Supervision for Brain Tumor Segmentation—0
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation—0
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
Brain Tumor Detection Based On Mathematical Analysis and Symmetry Information—0
Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities—0
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