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
SKM-TEA
KiTS19
ATLAS v2.0
HaN-Seg
BHSD
Latest papers
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.
A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis.
A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients.
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.
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.
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.
Enforcing temporal consistency in Deep Learning segmentation of brain MR images
bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets
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Proposed CNN based segmentation approaches demonstrate how 2D segmentation using prior slices can provide similar results to 3D segmentation while maintaining good continuity in the 3D dimension and improved speed.
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
mateuszbuda/brain-segmentation-pytorch
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Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.
Med3D: Transfer Learning for 3D Medical Image Analysis
The performance on deep learning is significantly affected by volume of training data.
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.
