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 implementations
13 papers
1,959
3 papers
4,873
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3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation

hidneuron/3d-effivitcaps 25 Mar 2024

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.

0
25 Mar 2024

MatchSeg: Towards Better Segmentation via Reference Image Matching

keeplearning-again/matchseg 23 Mar 2024

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.

7
23 Mar 2024

Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations

um2ii/sam_dataannotation 22 Mar 2024

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.

1
22 Mar 2024

H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation

wurenkai/h-vmunet 20 Mar 2024

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.

28
20 Mar 2024

Diversified and Personalized Multi-rater Medical Image Segmentation

ycwu1997/d-persona 20 Mar 2024

To address it, the common practice is to gather multiple annotations from different experts, leading to the setting of multi-rater medical image segmentation.

23
20 Mar 2024

Concatenate, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation

shumengli/cfr 17 Mar 2024

Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations.

2
17 Mar 2024

VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation

nobodyplayer1/vm-unetv2 14 Mar 2024

In the field of medical image segmentation, models based on both CNN and Transformer have been thoroughly investigated.

32
14 Mar 2024

FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images

arcadelab/fastsam3d 14 Mar 2024

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.

14
14 Mar 2024

Large Window-based Mamba UNet for Medical Image Segmentation: Beyond Convolution and Self-attention

wjh892521292/lma-unet 12 Mar 2024

In this paper, we introduce a Large Window-based Mamba U}-shape Network, or LMa-UNet, for 2D and 3D medical image segmentation.

18
12 Mar 2024

Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation

cai4cai/ace-dliris 11 Mar 2024

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.

4
11 Mar 2024