Medical Image Segmentation
732 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 implementationsDatasets
Subtasks
- Lesion Segmentation
- Brain Tumor Segmentation
- Cell Segmentation
- Brain Segmentation
- Brain Segmentation
- Skin Lesion Segmentation
- Retinal Vessel Segmentation
- Semi-supervised Medical Image Segmentation
- MRI segmentation
- Cardiac Segmentation
- 3D Medical Imaging Segmentation
- Liver Segmentation
- Volumetric Medical Image Segmentation
- Brain Image Segmentation
- Pancreas Segmentation
- Iris Segmentation
- Video Polyp Segmentation
- Lung Nodule Segmentation
- Nuclear Segmentation
- COVID-19 Image Segmentation
- Skin Cancer Segmentation
- Electron Microscopy Image Segmentation
- Ischemic Stroke Lesion Segmentation
- Brain Lesion Segmentation From Mri
- Placenta Segmentation
- Infant Brain Mri Segmentation
- Automatic Liver And Tumor Segmentation
- Acute Stroke Lesion Segmentation
- Cerebrovascular Network Segmentation
- Automated Pancreas Segmentation
- Semantic Segmentation Of Orthoimagery
- Pulmorary Vessel Segmentation
- Brain Ventricle Localization And Segmentation In 3D Ultrasound Images
Latest papers
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
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.
Concatenate, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation
Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations.
VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation
In the field of medical image segmentation, models based on both CNN and Transformer have been thoroughly investigated.
FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images
Segment anything models (SAMs) are gaining attention for their zero-shot generalization capability in segmenting objects of unseen classes and in unseen domains when properly prompted.
Large Window-based Mamba UNet for Medical Image Segmentation: Beyond Convolution and Self-attention
In this paper, we introduce a Large Window-based Mamba U}-shape Network, or LMa-UNet, for 2D and 3D medical image segmentation.
Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation
Using mL1-ACE, we reduce average and maximum calibration error by 45% and 55% respectively, maintaining a Dice score of 87% on the BraTS 2021 dataset.