Brain Tumor Segmentation
124 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 with no code
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser
Multimodal Magnetic Resonance (MR) Imaging plays a crucial role in disease diagnosis due to its ability to provide complementary information by analyzing a relationship between multimodal images on the same subject.
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI
Federated Learning (FL) enables collaborative model training among medical centers without sharing private data.
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet
Automated segmentation of distinct tumor regions is critical for accurate diagnosis and treatment planning in pediatric brain tumors.
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy
We have used the MSVD algorithm, reducing the image noise and then using the deep neural network to segment the tumor in the images.
Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images
Compared to the baseline U-Net and its variants, the models that learned edges along with the tumor regions performed well in core tumor regions in both training and validation datasets.
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations
This study was conducted on the Brain Tumor Segmentation (BraTS) Challenge Africa (BraTS-Africa) dataset, which provides a valuable resource for addressing challenges specific to resource-limited settings, particularly the African population, and facilitating the development of effective and more generalizable segmentation algorithms.
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)
Development of robust general purpose 3D segmentation frameworks using the latest deep learning techniques is one of the active topics in various bio-medical domains.
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks
We conduct experiments on two publicly available datasets, MICCAI's Brain Tumor Segmentation Challenge (BRATS), and Head and Neck Tumor Segmentation Challenge (HECKTOR), demonstrating the effectiveness of our method on different medical imaging modalities.
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation
When evaluating skin lesion and brain tumor segmentation datasets, we observe a remarkable improvement of 1. 71% in Intersection-over Union scores for skin lesion segmentation and of 8. 58% for brain tumor segmentation.
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning
This paper presents a method based on a kernel dictionary learning algorithm for segmenting brain tumor regions in magnetic resonance images (MRI).
