Tumor Segmentation Edit Task
Computer Vision • Semantic Segmentation
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| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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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
An attempt at beating the 3D U-Net
• MIC-DKFZ/nnunet
•
The U-Net is arguably the most successful segmentation architecture in the medical domain.
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
AUTOMATIC LIVER AND TUMOR SEGMENTATION LESION SEGMENTATION LIVER SEGMENTATION TUMOR SEGMENTATION
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
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
In the first step, we train a FCN to segment the liver as ROI input for a second FCN.
AUTOMATIC LIVER AND TUMOR SEGMENTATION LESION SEGMENTATION TUMOR SEGMENTATION
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge
The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem.
The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes
The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment.
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
3D MRI brain tumor segmentation using autoencoder regularization
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
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation.
DATA AUGMENTATION MEDICAL IMAGE SEGMENTATION TUMOR SEGMENTATION