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Found 4 papers, 3 papers with code
Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker
Significance: The proposed new methodology with publicly available volunteer data and code for parametersing the long-term dependency, experimentally shown to be valid sources of performance improvement, which could potentially lead to better model development and practical optimisation of the reconstruction application.
Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction
Three-dimensional (3D) freehand ultrasound (US) reconstruction without using any additional external tracking device has seen recent advances with deep neural networks (DNNs).
Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames
Little benefit was observed by adding frames more than one second away from the predicted transformation, with or without LSTM-based RNNs.
Intraoperative Liver Surface Completion with Graph Convolutional VAE
In this work we propose a method based on geometric deep learning to predict the complete surface of the liver, given a partial point cloud of the organ obtained during the surgical laparoscopic procedure.
