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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
1,988
PaddlePaddle/PaddleSeg
9 papers
8,230
MIC-DKFZ/nnunet
4 papers
5,005
black0017/MedicalZooPytorch
4 papers
1,607
See all 12 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

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).

12
Paper
Code

UNETR: Transformers for 3D Medical Image Segmentation

Project-MONAI/research-contributions • • 18 Mar 2021

Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem.

10
Paper
Code

nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

MIC-DKFZ/nnunet • • 27 Sep 2018

The U-Net was presented in 2015.

9
Paper
Code

Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

yulequan/UA-MT • • 16 Jul 2019

We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information.

8
Paper
Code

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

charan223/Brain-Tumor-Segmentation-using-Topological-Loss • • 1 Sep 2017

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.

7
Paper
Code

A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

nabsabraham/focal-tversky-unet • • 18 Oct 2018

We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation.

6
Paper
Code

MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation

nibtehaz/MultiResUNet • • ScienceDirect 2019

We have compared our proposed architecture MultiResUNet with the classical U-Net on a vast repertoire of multimodal medical images.

6
Paper
Code

ResUNet++: An Advanced Architecture for Medical Image Segmentation

DebeshJha/ResUNetplusplus • • 16 Nov 2019

Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer.

6
Paper
Code

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.

6
Paper
Code

Boundary loss for highly unbalanced segmentation

LIVIAETS/surface-loss • • 17 Dec 2018

We propose a boundary loss, which takes the form of a distance metric on the space of contours, not regions.

5
Paper
Code
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