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
AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation
However, we argue that the current design of the vision transformer-based UNet (ViT-UNet) segmentation models may not effectively handle the heterogeneous appearance (e. g., varying shapes and sizes) of objects of interest in medical image segmentation tasks.
Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
This paper introduces H-SAM: a prompt-free adaptation of SAM tailored for efficient fine-tuning of medical images via a two-stage hierarchical decoding procedure.
Generative Medical Segmentation
Concretely, GMS employs a robust pre-trained Variational Autoencoder (VAE) to derive latent representations of both images and masks, followed by a mapping model that learns the transition from image to mask in the latent space.
Segment Any Medical Model Extended
bingogome/samm
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To this end, a unified platform helps push the boundary of the foundation model for medical images, facilitating the use, modification, and validation of SAM and its variants in medical image segmentation.
Clustering Propagation for Universal Medical Image Segmentation
}$ This enables knowledge acquired from prior slices to assist in the segmentation of the current slice, further efficiently bridging the communication between remote slices using mere 2D networks.
3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation
Our encoder uses capsule blocks and EfficientViT blocks to jointly capture local and global semantic information more effectively and efficiently with less information loss, while the decoder employs CNN blocks and EfficientViT blocks to catch ffner details for segmentation.
MatchSeg: Towards Better Segmentation via Reference Image Matching
keeplearning-again/matchseg
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Few-shot learning aims to overcome the need for annotated data by using a small labeled dataset, known as a support set, to guide predicting labels for new, unlabeled images, known as the query set.
Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations
Curating annotations for medical image segmentation is a labor-intensive and time-consuming task that requires domain expertise, resulting in "narrowly" focused deep learning (DL) models with limited translational utility.
H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation
In the field of medical image segmentation, variant models based on Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs) as the base modules have been very widely developed and applied.
Diversified and Personalized Multi-rater Medical Image Segmentation
To address it, the common practice is to gather multiple annotations from different experts, leading to the setting of multi-rater medical image segmentation.
