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-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation
Recent developments underscore the potential of textual information in enhancing learning models for a deeper understanding of medical visual semantics.
Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings
The joint utilization of diverse data sources for medical imaging segmentation has emerged as a crucial area of research, aiming to address challenges such as data heterogeneity, domain shift, and data quality discrepancies.
Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation
This novel framework offers improved performance with fewer parameters and holds significant potential for accurate segmentation of lesion regions in various medical tasks, making it clinically valuable.
MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation
Medical image segmentation of anatomical structures and pathology is crucial in modern clinical diagnosis, disease study, and treatment planning.
Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets
Medical image segmentation presents the challenge of segmenting various-size targets, demanding the model to effectively capture both local and global information.
CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation
To address these problems, we present a pipeline that integrates the conditional diffusion model for lymph node generation and the nnU-Net model for lymph node segmentation to improve the segmentation performance of abdominal lymph nodes through synthesizing a diversity of realistic abdominal lymph node data.
Rotate to Scan: UNet-like Mamba with Triplet SSM Module for Medical Image Segmentation
Image segmentation holds a vital position in the realms of diagnosis and treatment within the medical domain.
Integrating Mamba Sequence Model and Hierarchical Upsampling Network for Accurate Semantic Segmentation of Multiple Sclerosis Legion
Integrating components from convolutional neural networks and state space models in medical image segmentation presents a compelling approach to enhance accuracy and efficiency.
SegICL: A Universal In-context Learning Framework for Enhanced Segmentation in Medical Imaging
Extensive experimental validation of SegICL demonstrates a positive correlation between the number of prompt samples and segmentation performance on OOD modalities and tasks.
EDUE: Expert Disagreement-Guided One-Pass Uncertainty Estimation for Medical Image Segmentation
Deploying deep learning (DL) models in medical applications relies on predictive performance and other critical factors, such as conveying trustworthy predictive uncertainty.
