Brain Tumor Segmentation
123 papers with code • 9 benchmarks • 4 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Brain Tumor Segmentation
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Libraries
Use these libraries to find Brain Tumor Segmentation models and implementations
BraTS 2017
BraTS 2015
BraTS 2013
BraTS 2014
Latest papers
Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentation
To this end, a multi-modal MR brain tumor segmentation method with tumor prototype-driven and multi-expert integration is proposed.
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor
Magnetic resonance imaging (MRI) is commonly used for brain tumor segmentation, which is critical for patient evaluation and treatment planning.
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks
bidur-khanal/mvaal-medical-images
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Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation.
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation
Magnetic resonance imaging (MRI) is a commonly used technique for brain tumor segmentation, which is critical for evaluating patients and planning treatment.
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models
Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80% - 90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting
brats-inpainting/2023_challenge
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The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada.
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation
aecelaya/mg-nets
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Accurate medical imaging segmentation is critical for precise and effective medical interventions.
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities
ccarliu/m3ae
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In the first stage, a multimodal masked autoencoder (M3AE) is proposed, where both random modalities (i. e., modality dropout) and random patches of the remaining modalities are masked for a reconstruction task, for self-supervised learning of robust multimodal representations against missing modalities.
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentation
ysmei97/multimodal_pci_mask
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Our experimental results on the Multi-modal Brain Tumor Segmentation Challenge (BraTS) datasets outperform those of state-of-the-art segmentation baselines, with validation Dice similarity coefficients of 0. 920, 0. 897, 0. 837 for the whole tumor, tumor core, and enhancing tumor on BraTS-2020.
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis
RL4M/PCRLv2
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Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views.
