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
Most implemented papers
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields.
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
To illustrate its efficiency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images.
Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation
MiguelMonteiro/CRFasRNNLayer
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In this paper, we test whether this algorithm, which was shown to improve semantic segmentation for 2D RGB images, is able to improve segmentation quality for 3D multi-modal medical images.
nnFormer: Interleaved Transformer for Volumetric Segmentation
Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community.
LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image Segmentation
xmindflow/lhunet
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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.
3D Densely Convolutional Networks for VolumetricSegmentation
black0017/MedicalZooPytorch
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arXiv preprint 2017
The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
jeya-maria-jose/KiU-Net-pytorch
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To overcome this issue, we propose using an overcomplete convolutional architecture where we project our input image into a higher dimension such that we constrain the receptive field from increasing in the deep layers of the network.
Positional Contrastive Learning for Volumetric Medical Image Segmentation
The success of deep learning heavily depends on the availability of large labeled training sets.
A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation
We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume.
Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images
In this work, we propose a novel memory-efficient network architecture for 3D high-resolution image segmentation.
