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
Latest papers with no code
Cats: Complementary CNN and Transformer Encoders for Segmentation
We fuse the information from the convolutional encoder and the transformer, and pass it to the decoder to obtain the results.
A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs
In this approach, the convolutional layers at the deepest level of the UNet are replaced by a GNN-based module with a series of graph convolutions.
Pulmonary Artery–Vein Classification in CT Images Using Deep Learning
In this paper, we present a novel, fully automatic approach to classify vessels from chest CT images into arteries and veins.
Spatial Aggregation of Holistically-Nested Convolutional Neural Networks for Automated Pancreas Localization and Segmentation
Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis.
