Brain Tumor Segmentation Edit Task
Medical • Medical Image Segmentation
About
Brain tumor segmentation is the task of segmenting tumors from other brain artefacts in MRI image of the brain.
( Image credit: Brain Tumor Segmentation with Deep Neural Networks )
Benchmarks
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Datasets
Greatest papers with code
nnU-Net for Brain Tumor Segmentation
• MIC-DKFZ/nnunet
• 
We apply nnU-Net to the segmentation task of the BraTS 2020 challenge.
BRAIN TUMOR SEGMENTATION DATA AUGMENTATION TUMOR SEGMENTATION
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
Ranked #1 on
Lesion Segmentation
on ISLES-2015
3D MEDICAL IMAGING SEGMENTATION BRAIN LESION SEGMENTATION FROM MRI BRAIN TUMOR SEGMENTATION LESION SEGMENTATION
3D MRI brain tumor segmentation using autoencoder regularization
• black0017/MedicalZooPytorch
•
Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease.
Ranked #1 on
Brain Tumor Segmentation
on BRATS 2018
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.
Ranked #1 on
Lung Nodule Segmentation
on LIDC-IDRI
BRAIN TUMOR SEGMENTATION LIVER SEGMENTATION LUNG NODULE DETECTION LUNG NODULE SEGMENTATION PULMONARY EMBOLISM DETECTION SELF-SUPERVISED LEARNING TRANSFER LEARNING
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
• mateuszbuda/brain-segmentation-pytorch
•
Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.
Ranked #2 on
Brain Segmentation
on Brain MRI segmentation
3D MEDICAL IMAGING SEGMENTATION BRAIN SEGMENTATION BRAIN TUMOR SEGMENTATION TUMOR SEGMENTATION TWO-SAMPLE TESTING
Multi-scale self-guided attention for medical image segmentation
arXiv preprint 2019
• sinAshish/Multi-Scale-Attention
•
In this paper we attempt to overcome these limitations with the proposed architecture, by capturing richer contextual dependencies based on the use of guided self-attention mechanisms.
Ranked #1 on
Medical Image Segmentation
on HSVM
ATTENTIVE SEGMENTATION NETWORKS BRAIN TUMOR SEGMENTATION DEEP ATTENTION
Brain Tumor Segmentation with Deep Neural Networks
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.
Ranked #1 on
Brain Tumor Segmentation
on BRATS-2013 leaderboard
Autofocus Layer for Semantic Segmentation
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.
Ranked #3 on
Brain Tumor Segmentation
on BRATS-2015
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
• pykao/Modified-3D-UNet-Pytorch
•
Quantitative analysis of brain tumors is critical for clinical decision making.
Ranked #2 on
Brain Tumor Segmentation
on BRATS-2015
BRAIN TUMOR SEGMENTATION DATA AUGMENTATION DECISION MAKING TUMOR SEGMENTATION
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
• jeya-maria-jose/KiU-Net-pytorch
•
To overcome this issue, we propose using an overcomplete convolutional architecture where we project our input image into a higher dimension such that we constrain the receptive field from increasing in the deep layers of the network.
Ranked #1 on
Medical Image Segmentation
on RITE
3D MEDICAL IMAGING SEGMENTATION BRAIN TUMOR SEGMENTATION COMPUTED TOMOGRAPHY (CT) LIVER SEGMENTATION SEMANTIC SEGMENTATION ULTRASOUND VOLUMETRIC MEDICAL IMAGE SEGMENTATION