Medical Image Segmentation
302 papers with code • 33 benchmarks • 32 datasets
Medical image segmentation is the task of segmenting objects of interest in a medical image.
( Image credit: IVD-Net )
- Lesion Segmentation
- Brain Tumor Segmentation
- Brain Segmentation
- Cell Segmentation
- Cell Segmentation
- Retinal Vessel Segmentation
- Skin Lesion Segmentation
- 3D Medical Imaging Segmentation
- MRI segmentation
- Liver Segmentation
- Cardiac Segmentation
- Iris Segmentation
- Semi-supervised Medical Image Segmentation
- Brain Image Segmentation
- Video Polyp Segmentation
- Pancreas Segmentation
- COVID-19 Image Segmentation
- Lung Nodule Segmentation
- Volumetric Medical Image Segmentation
- Skin Cancer Segmentation
- Nuclear Segmentation
- Electron Microscopy Image Segmentation
- Infant Brain Mri Segmentation
- Brain Lesion Segmentation From Mri
- Ischemic Stroke Lesion Segmentation
- Automatic Liver And Tumor Segmentation
- Acute Stroke Lesion Segmentation
- Cerebrovascular Network Segmentation
- Automated Pancreas Segmentation
- Placenta Segmentation
- Pulmorary Vessel Segmentation
- Brain Ventricle Localization And Segmentation In 3D Ultrasound Images
- Semantic Segmentation Of Orthoimagery
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields.
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.
UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation.
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical 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.
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.