3D Medical Imaging Segmentation
32 papers with code • 1 benchmarks • 9 datasets
3D medical imaging segmentation is the task of segmenting medical objects of interest from 3D medical imaging.
( Image credit: Elastic Boundary Projection for 3D Medical Image Segmentation )
Benchmarks
These leaderboards are used to track progress in 3D Medical Imaging Segmentation
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Libraries
Use these libraries to find 3D Medical Imaging Segmentation models and implementationsDatasets
VerSe
KiTS19
SKM-TEA
ATLAS v2.0
HaN-Seg
BHSD
Most implemented papers
Elastic Boundary Projection for 3D Medical Image Segmentation
twni2016/Elastic-Boundary-Projection
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CVPR 2019
The key observation is that, although the object is a 3D volume, what we really need in segmentation is to find its boundary which is a 2D surface.
Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images
The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilage with diffuse boundaries and inhomogeneous intensities.
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
shreyaspadhy/unet-zoo
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NeurIPS 2016
Segmentation of 3D images is a fundamental problem in biomedical image analysis.
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.
TernaryNet: Faster Deep Model Inference without GPUs for Medical 3D Segmentation using Sparse and Binary Convolutions
We propose a new scheme that approximates both trainable weights and neural activations in deep networks by ternary values and tackles the open question of backpropagation when dealing with non-differentiable functions.
An application of cascaded 3D fully convolutional networks for medical image segmentation
In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models.
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning
arnab39/FewShot_GAN-Unet3D
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In addition, our work presents a comprehensive analysis of different GAN architectures for semi-supervised segmentation, showing recent techniques like feature matching to yield a higher performance than conventional adversarial training approaches.
A New Ensemble Learning Framework for 3D Biomedical Image Segmentation
In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models.
Linking convolutional neural networks with graph convolutional networks: application in pulmonary artery-vein separation
chushan89/Linking-CNN-GCN
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Preprint 2019
In conclusion, the proposed CNN-GCN method combines local image information with graph connectivity information, improving pulmonary A/V separation over a baseline CNN method, approaching the performance of human observers.
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks
yucornetto/CAKES
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3D Convolution Neural Networks (CNNs) have been widely applied to 3D scene understanding, such as video analysis and volumetric image recognition.
