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Found 4 papers, 0 papers with code
Multi-task Localization and Segmentation for X-ray Guided Planning in Knee Surgery
A deep multi-task stacked hourglass network is trained on 149 conventional lateral X-ray images to jointly localize two femoral landmarks, to predict a region of interest for the posterior femoral cortex tangent line, and to perform semantic segmentation of the femur, patella, tibia, and fibula with adaptive task complexity weighting.
Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration
For single-vertebra registration, we achieve an accuracy of 0. 74$\pm$0. 26 mm and highly improved robustness.
Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks
Therefore, accurate and reliable lane marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals.
Dilated FCN for Multi-Agent 2D/3D Medical Image Registration
Second, to handle various artifacts in 2D X-ray images, multiple local agents are employed efficiently via FCN-based structures, and an auto attention mechanism is proposed to favor the proposals from regions with more reliable visual cues.
