Medical Image Segmentation
767 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
GlaS
Kvasir
Medical Segmentation Decathlon
PROMISE12
AMOS
CHASE_DB1
CVC-ClinicDB
ACDC
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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Semi-supervised Medical Image Segmentation
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Retinal Vessel 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
HC-Mamba: Vision MAMBA with Hybrid Convolutional Techniques for Medical Image Segmentation
By combining dilated convolution and depthwise separable convolutions, HC-Mamba is able to process large-scale medical image data at a much lower computational cost while maintaining a high level of performance.
Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI
Results indicate a remarkable performance, with 99% accuracy for tumor segmentation, 98% accuracy for tumor classification, and 99% accuracy for tumor localization achieved with the proposed approach.
On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks
This work proposes a brain tumor segmentation method as part of the BraTS-GoAT challenge.
DmADs-Net: Dense multiscale attention and depth-supervised network for medical image segmentation
In addition, in the feature fusion phase, a Feature Refinement and Fusion Block is created to enhance the fusion of different semantic information. We validated the performance of the network using five datasets of varying sizes and types.
Segmentation Quality and Volumetric Accuracy in Medical Imaging
Current medical image segmentation relies on the region-based (Dice, F1-score) and boundary-based (Hausdorff distance, surface distance) metrics as the de-facto standard.
DiffSeg: A Segmentation Model for Skin Lesions Based on Diffusion Difference
However, the accuracy of the segmentation results is often limited by insufficient supervision and the complex nature of medical imaging.
Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models
Despite the remarkable success of deep learning in medical imaging analysis, medical image segmentation remains challenging due to the scarcity of high-quality labeled images for supervision.
Mitigating False Predictions In Unreasonable Body Regions
This limitation leads to false predictions when applied to body regions beyond the FOV of the training data.
Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images
To address these issues, in this paper, we develop a novel Breast Ultrasound SAM Adapter, termed Breast Ultrasound Segment Anything Model (BUSSAM), which migrates the SAM to the field of breast ultrasound image segmentation by using the adapter technique.
CFPFormer: Feature-pyramid like Transformer Decoder for Segmentation and Detection
Feature pyramids have been widely adopted in convolutional neural networks (CNNs) and transformers for tasks like medical image segmentation and object detection.
