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 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.
SOTA for Pancreas Segmentation on CT-150
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.
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
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
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.
#2 best model for
Brain Tumor Segmentation
on BRATS-2015
3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data.
Data augmentation using learned transformations for one-shot medical image segmentation
Image segmentation is an important task in many medical applications.