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Medical Image Segmentation

751 papers with code • 44 benchmarks • 43 datasets

Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.

( Image credit: IVD-Net )

Libraries

Use these libraries to find Medical Image Segmentation models and implementations
HiLab-git/SSL4MIS
13 papers
1,993
PaddlePaddle/PaddleSeg
9 papers
8,252
MIC-DKFZ/nnunet
4 papers
5,030
black0017/MedicalZooPytorch
4 papers
1,611
See all 12 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

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.

476
Paper
Code

Densely Connected Convolutional Networks

liuzhuang13/DenseNet • • CVPR 2017

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.

143
Paper
Code

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

google-research/vision_transformer • • ICLR 2021

While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.

143
Paper
Code

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.

74
Paper
Code

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.

36
Paper
Code

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

33
Paper
Code

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.

27
Paper
Code

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.

20
Paper
Code

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.

14
Paper
Code

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.

12
Paper
Code
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