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Found 6 papers, 1 papers with code
Aladdin: Joint Atlas Building and Diffeomorphic Registration Learning with Pairwise Alignment
Atlas building and image registration are important tasks for medical image analysis.
VoteNet++: Registration Refinement for Multi-Atlas Segmentation
Multi-atlas segmentation (MAS) is a popular image segmentation technique for medical images.
Local Temperature Scaling for Probability Calibration
Thus, we propose a learning-based calibration method that focuses on multi-label semantic segmentation.
VoteNet+ : An Improved Deep Learning Label Fusion Method for Multi-atlas Segmentation
Specifically, we first illustrate that using a deep convolutional neural network to predict atlas probabilities can better distinguish correct atlas labels from incorrect ones than relying on image intensity difference as is typical in JLF.
VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation
Experiments on 3D brain MRI data show that by selecting a good initial atlas set MAS with VoteNet significantly outperforms a number of other label fusion strategies as well as a direct DL segmentation approach.
Fast Predictive Simple Geodesic Regression
Deformable image registration and regression are important tasks in medical image analysis.
