no code implementations • 26 Apr 2022 • Halie M. Rando, Christian Brueffer, Ronan Lordan, Anna Ada Dattoli, David Manheim, Jesse G. Meyer, Ariel I. Mundo, Dimitri Perrin, David Mai, Nils Wellhausen, COVID-19 Review Consortium, Anthony Gitter, Casey S. Greene
These two categories of tests provide different perspectives valuable to understanding the spread of SARS-CoV-2.
The recent application of deep learning technologies in medical image registration has exponentially decreased the registration time and gradually increased registration accuracy when compared to their traditional counterparts.
In both resolutions, the proposed DenseDeformation network outperforms VoxelMorph in registration accuracy.
In this paper, we investigate and compare the performance of a deep learning based registration method with traditional optimization based methods on samples from tissue-clearing methods.
Recent progress in tissue clearing has allowed for the imaging of entire organs at single-cell resolution.
1 code implementation • 5 Oct 2017 • Jens B. Stephansen, Alexander N. Olesen, Mads Olsen, Aditya Ambati, Eileen B. Leary, Hyatt E. Moore, Oscar Carrillo, Ling Lin, Fang Han, Han Yan, Yun L. Sun, Yves Dauvilliers, Sabine Scholz, Lucie Barateau, Birgit Hogl, Ambra Stefani, Seung Chul Hong, Tae Won Kim, Fabio Pizza, Giuseppe Plazzi, Stefano Vandi, Elena Antelmi, Dimitri Perrin, Samuel T. Kuna, Paula K. Schweitzer, Clete Kushida, Paul E. Peppard, Helge B. D. Sorensen, Poul Jennum, Emmanuel Mignot
Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians.