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 )

Libraries

Use these libraries to find Medical Image Segmentation models and implementations

Most implemented papers

U-Net: Convolutional Networks for Biomedical Image Segmentation

labmlai/annotated_deep_learning_paper_implementations 18 May 2015

There is large consent that successful training of deep networks requires many thousand annotated training samples.

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

PaddlePaddle/PaddleSeg 2 Nov 2015

We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.

Attention U-Net: Learning Where to Look for the Pancreas

ozan-oktay/Attention-Gated-Networks 11 Apr 2018

We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

MrGiovanni/Nested-UNet 18 Jul 2018

Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet

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

faustomilletari/VNet 15 Jun 2016

Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields.

Brain Tumor Segmentation with Deep Neural Networks

naldeborgh7575/brain_segmentation 13 May 2015

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 3+: A Full-Scale Connected UNet for Medical Image Segmentation

ZJUGiveLab/UNet-Version 19 Apr 2020

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

LeeJunHyun/Image_Segmentation 20 Feb 2018

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.

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

MrGiovanni/UNetPlusPlus 11 Dec 2019

The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).

TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Beckschen/TransUNet 8 Feb 2021

Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.