Browse > Medical > Medical Image Segmentation

Medical Image Segmentation

32 papers with code · Medical

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

Greatest papers with code

U-Net: Convolutional Networks for Biomedical Image Segmentation

18 May 2015orobix/retina-unet

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently.

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

11 Apr 2018ozan-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. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task.

PANCREAS SEGMENTATION SEMANTIC SEGMENTATION

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

15 Jun 2016mattmacy/vnet.pytorch

Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes.

SEMANTIC SEGMENTATION VOLUMETRIC MEDICAL IMAGE SEGMENTATION

Brain Tumor Segmentation with Deep Neural Networks

13 May 2015naldeborgh7575/brain_segmentation

Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. 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.

BRAIN TUMOR SEGMENTATION

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

20 Feb 2018LeeJunHyun/Image_Segmentation

More specifically, these techniques have been successfully applied to medical image classification, segmentation, and detection tasks. 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.

IMAGE CLASSIFICATION LESION SEGMENTATION LUNG NODULE SEGMENTATION RETINAL VESSEL SEGMENTATION SEMANTIC SEGMENTATION SKIN CANCER SEGMENTATION

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

1 Sep 2017taigw/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. The whole tumor is segmented in the first step and the bounding box of the result is used for the tumor core segmentation in the second step.

BRAIN TUMOR SEGMENTATION

Autofocus Layer for Semantic Segmentation

22 May 2018yaq007/Autofocus-Layer

We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing. Autofocus layers adaptively change the size of the effective receptive field based on the processed context to generate more powerful features.

BRAIN TUMOR SEGMENTATION SEMANTIC SEGMENTATION

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study

12 Dec 2016josedolz/LiviaNET

This study investigates a 3D and fully convolutional neural network (CNN) for subcortical brain structure segmentation in MRI. To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data.

3D MEDICAL IMAGING SEGMENTATION

An application of cascaded 3D fully convolutional networks for medical image segmentation

14 Mar 2018holgerroth/3Dunet_abdomen_cascade

In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models. This reduces the number of voxels the second FCN has to classify to ~10% and allows it to focus on more detailed segmentation of the organs and vessels.

3D MEDICAL IMAGING SEGMENTATION SEMANTIC SEGMENTATION

Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

29 Oct 2018arnab39/FewShot_GAN-Unet3D

Leveraging the recent success of adversarial learning for semi-supervised segmentation, we propose a novel method based on Generative Adversarial Networks (GANs) to train a segmentation model with both labeled and unlabeled images. In addition, our work presents a comprehensive analysis of different GAN architectures for semi-supervised segmentation, showing recent techniques like feature matching to yield a higher performance than conventional adversarial training approaches.

3D MEDICAL IMAGING SEGMENTATION SEMANTIC SEGMENTATION