Brain Tumor Segmentation
126 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
Most implemented papers
The Federated Tumor Segmentation (FeTS) Challenge
The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities
Wangyixinxin/ACN
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Specifically, ACN adopts a novel co-training network, which enables a coupled learning process for both full modality and missing modality to supplement each other's domain and feature representations, and more importantly, to recover the `missing' information of absent modalities.
Optimized U-Net for Brain Tumor Segmentation
NVIDIA/DeepLearningExamples
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We propose an optimized U-Net architecture for a brain tumor segmentation task in the BraTS21 challenge.
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
Project-MONAI/research-contributions
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Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity.
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation
Specifically, we propose a linearly scalable approach to context modeling, formulating Nonnegative Matrix Factorization (NMF) as a differentiable layer integrated into a U-shaped architecture.
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
Inspired by the success of DPM, we propose the first DPM based model toward general medical image segmentation tasks, which we named MedSegDiff.
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy.
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss
milliondegree/semantic-segmentation-tensorflow
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Since the proposal of fully convolutional neural network (FCNN), it has been widely used in semantic segmentation because of its high accuracy of pixel-wise classification as well as high precision of localization.
Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI
However, this is not optimal in FCN due to the spatial correspondence between units and voxels.
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction
pykao/BraTS2018-tumor-segmentation
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For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1mm space and map this parcellation to each individual subject data.
