1 code implementation • 16 Oct 2023 • Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, Yipeng Hu
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.
1 code implementation • 20 Aug 2023 • Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, Yipeng Hu
Three-dimensional (3D) freehand ultrasound (US) reconstruction without using any additional external tracking device has seen recent advances with deep neural networks (DNNs).
1 code implementation • 9 Nov 2022 • Qi Li, Ziyi Shen, Qian Li, Dean C Barratt, Thomas Dowrick, Matthew J Clarkson, Tom Vercauteren, Yipeng Hu
Little benefit was observed by adding frames more than one second away from the predicted transformation, with or without LSTM-based RNNs.
no code implementations • 8 Sep 2020 • Simone Foti, Bongjin Koo, Thomas Dowrick, Joao Ramalhinho, Moustafa Allam, Brian Davidson, Danail Stoyanov, Matthew J. Clarkson
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.