Brain Tumor Segmentation
127 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
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.
Scratch Each Other's Back: Incomplete Multi-Modal Brain Tumor Segmentation via Category Aware Group Self-Support Learning
qysgithubopen/gss
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ICCV 2023
In this paper, considering the sensitivity of different modalities to diverse tumor regions, we propose a Category Aware Group Self-Support Learning framework, called GSS, to make up for the information deficit among the modalities in the individual modal feature extraction phase.
Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?
Our experiments show that the choice of a BE method can compromise up to 15. 7% of the tumor segmentation performance.
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision
Then, by teaching the model to convert images across modalities, we leverage available pixel-level annotations from the source modality to enable segmentation in the unannotated target modality.
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.
Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation
himashi92/vizviva_fets_2022
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As intensities of MRI volumes are inconsistent across institutes, it is essential to extract universal features of multi-modal MRIs to precisely segment brain tumors.
NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation
Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate brain tumors by providing rich complementary information.
SFusion: Self-attention based N-to-One Multimodal Fusion Block
To solve this problem, we propose a self-attention based fusion block called SFusion.
