Volumetric Medical Image Segmentation
20 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Volumetric Medical Image Segmentation
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Libraries
Use these libraries to find Volumetric Medical Image Segmentation models and implementations
PROMISE12
Radio-Freqency Ultrasound volume dataset for pre-clinical liver tumors
Latest papers
LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image Segmentation
As a result of the rise of Transformer architectures in medical image analysis, specifically in the domain of medical image segmentation, a multitude of hybrid models have been created that merge the advantages of Convolutional Neural Networks (CNNs) and Transformers.
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation
sotiraslab/dlk
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D-Net is able to effectively utilize a multi-scale large receptive field and adaptively harness global contextual information.
SegVol: Universal and Interactive Volumetric Medical Image Segmentation
baai-dcai/segvol
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Precise image segmentation provides clinical study with instructive information.
CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation
Both 3D and purely 2D deep learning-based segmentation methods are deficient in dealing with such volumetric data since the performance of 3D methods suffers when confronting anisotropic data, and 2D methods disregard crucial volumetric information.
Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
xmed-lab/GenericSSL
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NeurIPS 2023
As a result, there is growing interest in using semi-supervised learning (SSL) techniques to train models with limited labeled data.
Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation
Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data especially on the task of volumetric medical image segmentation.
Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation
asif-hanif/vafa
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While recent advances in deep learning have improved the performance of volumetric medical image segmentation models, these models cannot be deployed for real-world applications immediately due to their vulnerability to adversarial attacks.
Learnable Weight Initialization for Volumetric Medical Image Segmentation
Hybrid volumetric medical image segmentation models, combining the advantages of local convolution and global attention, have recently received considerable attention.
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
MIC-DKFZ/MedNeXt
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This leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation.
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
In this study, we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg), a $\textbf{unified}$ UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation.
