Medical Image Segmentation
Medical image segmentation is the task of segmenting objects of interest in a medical image - for example organs or lesions.
State-of-the-art leaderboards
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Greatest papers with code
U-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires many thousand annotated training samples.
CELL SEGMENTATION DATA AUGMENTATION ELECTRON MICROSCOPY IMAGE SEGMENTATION LESION SEGMENTATION LUNG NODULE SEGMENTATION PANCREAS SEGMENTATION RETINAL VESSEL SEGMENTATION SEMANTIC SEGMENTATION SKIN CANCER SEGMENTATION
Attention U-Net: Learning Where to Look for the Pancreas
• ozan-oktay/Attention-Gated-Networks
• 
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
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.
SOTA for Lesion Segmentation on ISLES-2015
3D MEDICAL IMAGING SEGMENTATION BRAIN LESION SEGMENTATION FROM MRI BRAIN TUMOR SEGMENTATION LESION SEGMENTATION
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
• LeeJunHyun/Image_Segmentation
•
In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
SOTA for Lung Nodule Segmentation on LUNA
IMAGE CLASSIFICATION LESION SEGMENTATION LUNG NODULE SEGMENTATION RETINAL VESSEL SEGMENTATION SEMANTIC SEGMENTATION SKIN CANCER SEGMENTATION
nnU-Net: Breaking the Spell on Successful Medical Image Segmentation
• MIC-DKFZ/nnunet
•
Fueled by the diversity of datasets, semantic segmentation is a popular subfield in medical image analysis with a vast number of new methods being proposed each year.
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields.
Data augmentation using learned transformations for one-shot medical image segmentation
CVPR 2019 • xamyzhao/brainstorm
Image segmentation is an important task in many medical applications.
DATA AUGMENTATION MEDICAL IMAGE SEGMENTATION SEMANTIC SEGMENTATION
Brain Tumor Segmentation with Deep Neural Networks
• naldeborgh7575/brain_segmentation
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
• taigw/brats17
• 
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.
Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters
We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms.
FACIAL LANDMARK DETECTION MEDICAL IMAGE SEGMENTATION SEMANTIC SEGMENTATION