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 )
Benchmarks
These leaderboards are used to track progress in Medical Image Segmentation
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Libraries
Use these libraries to find Medical Image Segmentation models and implementationsDatasets
DRIVE
Kvasir-SEG
Kvasir
GlaS
Medical Segmentation Decathlon
PROMISE12
AMOS
CHASE_DB1
ACDC
CVC-ClinicDB
Subtasks
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Lesion Segmentation
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Brain Tumor Segmentation
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Cell Segmentation
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Skin Lesion Segmentation
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Skin Lesion Segmentation
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Brain Segmentation
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Retinal Vessel Segmentation
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Semi-supervised Medical Image Segmentation
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MRI segmentation
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Cardiac Segmentation
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3D Medical Imaging Segmentation
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Liver Segmentation
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Volumetric Medical Image Segmentation
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Brain Image Segmentation
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Pancreas Segmentation
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Iris Segmentation
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Video Polyp Segmentation
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Lung Nodule Segmentation
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Nuclear Segmentation
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COVID-19 Image Segmentation
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Skin Cancer Segmentation
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Electron Microscopy Image Segmentation
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Ischemic Stroke Lesion Segmentation
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Brain Lesion Segmentation From Mri
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Placenta Segmentation
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Infant Brain Mri Segmentation
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Automatic Liver And Tumor Segmentation
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Acute Stroke Lesion Segmentation
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Cerebrovascular Network Segmentation
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Automated Pancreas Segmentation
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Semantic Segmentation Of Orthoimagery
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Pulmorary Vessel Segmentation
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Brain Ventricle Localization And Segmentation In 3D Ultrasound Images
Latest papers with no code
Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation
In this paper, we introduce a novel Cross-model Mutual learning framework for Exemplar-based Medical image Segmentation (CMEMS), which leverages two models to mutually excavate implicit information from unlabeled data at multiple granularities.
Mixed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation
The Mean Teacher generates prototypes for labeled and unlabeled data, while the auxiliary network produces additional prototypes for mixed data processed by CutMix.
Q2A: Querying Implicit Fully Continuous Feature Pyramid to Align Features for Medical Image Segmentation
Therefore, we propose Q2A, a novel one-step query-based aligning paradigm, to solve the feature misalignment problem in the INR-based decoder.
Multi-rater Prompting for Ambiguous Medical Image Segmentation
In this paper, we tackle two challenges arisen in multi-rater annotations for medical image segmentation (called ambiguous medical image segmentation): (1) How to train a deep learning model when a group of raters produces a set of diverse but plausible annotations, and (2) how to fine-tune the model efficiently when computation resources are not available for re-training the entire model on a different dataset domain.
LUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image Segmentation
In this study, the performance of existing U-shaped neural network architectures was enhanced for medical image segmentation by adding Transformer.
An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentation
Additionally, the evidential fusion branch capitalizes on the complementary attributes of the first two branches and leverages an evidence-based Dempster-Shafer fusion strategy, supervised by more reliable and accurate pseudo-labels of unlabeled data.
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
Finally, SAM is prompted by the retrieved ROI to segment a specific organ.
EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentation
Although current semi-supervised medical segmentation methods can achieve decent performance, they are still affected by the uncertainty in unlabeled data and model predictions, and there is currently a lack of effective strategies that can explore the uncertain aspects of both simultaneously.
Uncertainty-aware Evidential Fusion-based Learning for Semi-supervised Medical Image Segmentation
Although the existing uncertainty-based semi-supervised medical segmentation methods have achieved excellent performance, they usually only consider a single uncertainty evaluation, which often fails to solve the problem related to credibility completely.
Towards Efficient and Accurate CT Segmentation via Edge-Preserving Probabilistic Downsampling
Downsampling images and labels, often necessitated by limited resources or to expedite network training, leads to the loss of small objects and thin boundaries.
