Brain Tumor Segmentation

115 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 )


Use these libraries to find Brain Tumor Segmentation models and implementations

Most implemented papers

Attention U-Net: Learning Where to Look for the Pancreas

ozan-oktay/Attention-Gated-Networks 11 Apr 2018

We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.

Brain Tumor Segmentation with Deep Neural Networks

naldeborgh7575/brain_segmentation 13 May 2015

Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN.

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

charan223/Brain-Tumor-Segmentation-using-Topological-Loss 1 Sep 2017

A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core.

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

pykao/Modified-3D-UNet-Pytorch 28 Feb 2018

Quantitative analysis of brain tumors is critical for clinical decision making.

3D MRI brain tumor segmentation using autoencoder regularization

black0017/MedicalZooPytorch 27 Oct 2018

Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease.

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

mateuszbuda/brain-segmentation 9 Jun 2019

Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.

nnU-Net for Brain Tumor Segmentation

MIC-DKFZ/nnunet 2 Nov 2020

We apply nnU-Net to the segmentation task of the BraTS 2020 challenge.

A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation

shu-hai/two-stage-VAE-Attention-gate-BraTS2020 4 Nov 2020

Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning.

Autofocus Layer for Semantic Segmentation

yaq007/Autofocus-Layer 22 May 2018

We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.

Diffusion Models for Implicit Image Segmentation Ensembles

juliawolleb/diffusion-based-segmentation 6 Dec 2021

By modifying the training and sampling scheme, we show that diffusion models can perform lesion segmentation of medical images.