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Brain Tumor Segmentation

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하위 태스크: 0
평가표: 12
보존된 평가 기록: 27

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.

<span style="color:grey; opacity: 0.6">( Image credit: Brain Tumor Segmentation with Deep Neural Networks )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://git.fmrib.ox.ac.uk/vaanathi/truenet_tumseg

공식 코드

Brain tumour segmentation using a triplanar ensemble of U-Nets

https://github.com/920232796/nestedformer

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NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation

https://github.com/AKuzina/DWP

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Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems

https://github.com/BRML/CNNbasedMedicalSegmentation

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CNN-based Segmentation of Medical Imaging Data

https://github.com/CAMERA-MRI/SPARK2023/tree/main/SPARK_BTS_KIFARU

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Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan Africa

https://github.com/CUMC-Yuan-Lab/GCML/tree/main/SANet/BraTS

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Automatic Brain Tumor Segmentation with Scale Attention Network

https://github.com/China-LiuXiaopeng/BraTS-DMFNet

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3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI

https://github.com/FETS-AI/Challenge

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The Federated Tumor Segmentation (FeTS) Challenge

https://github.com/FETS-AI/Front-End

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The Federated Tumor Segmentation (FeTS) Challenge

https://github.com/Guo-Xiaoqing/BraTS_OS

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Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction

https://github.com/HealthML/self-supervised-3d-tasks

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3D Self-Supervised Methods for Medical Imaging

https://github.com/HiLab-git/SSL4MIS

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All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation

https://github.com/JLiangLab/SemanticGenesis

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Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

https://github.com/JoetheReindeer/multiPI_TransBTS

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multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical Information

https://github.com/JohnleeHIT/Brats2019

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Multi-step Cascaded Networks for Brain Tumor Segmentation

https://github.com/KeleiHe/BiGL

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Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation

https://github.com/Luc4Gui/CAVM

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CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI Synthesis

https://github.com/LucasFidon/GeneralizedWassersteinDiceLoss

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Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge

https://github.com/LucasFidon/HardnessWeightedSampler

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Distributionally Robust Deep Learning using Hardness Weighted Sampling

https://github.com/MIC-DKFZ/nnunet

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nnU-Net for Brain Tumor Segmentation

https://github.com/MrGiovanni/ModelsGenesis

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Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

https://github.com/NVIDIA/DeepLearningExamples

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Optimized U-Net for Brain Tumor Segmentation

https://github.com/Precision-Medical-Imaging-Group/BraTS2023-inferCode

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Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging

https://github.com/Precision-Medical-Imaging-Group/BraTS2024-PEDS

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Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation

https://github.com/Project-MONAI/research-contributions/tree/master/SwinUNETR/BRATS21

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Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

https://github.com/RL4M/PCRLv2

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PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis

https://github.com/RanSuLab/RAUNet-tumor-segmentation

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RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

https://github.com/ShawkhIbneRashid/medical-images-sr

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Single MR Image Super-Resolution using Generative Adversarial Network

https://github.com/TaehwanY98/Fed-Ref

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FedRef: Communication-Efficient Bayesian Fine Tuning with Reference Model

https://github.com/Wangyixinxin/ACN

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ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities

https://github.com/Wenxuan-1119/TransBTS

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TransBTS: Multimodal Brain Tumor Segmentation Using Transformer

https://github.com/ZhihuaLiuEd/SoTA-Brain-Tumor-Segmentation

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Deep Learning Based Brain Tumor Segmentation: A Survey

https://github.com/ZhihuaLiuEd/canetbrats

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CANet: Context Aware Network for 3D Brain Glioma Segmentation

https://github.com/aecelaya/mg-nets

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FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation

https://github.com/arcadelab/counterfactual_diffusion

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Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs

https://github.com/arnold-jun/fed-munet

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Fed-MUnet: Multi-modal Federated Unet for Brain Tumor Segmentation

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