1 code implementation • 8 Dec 2023 • S. Mazdak Abulnaga, Neel Dey, Sean I. Young, Eileen Pan, Katherine I. Hobgood, Clinton J. Wang, P. Ellen Grant, Esra Abaci Turk, Polina Golland
In this work, we propose a machine learning segmentation framework for placental BOLD MRI and apply it to segmenting each volume in a time series.
no code implementations • 8 Dec 2023 • Pablo Laso, Stefano Cerri, Annabel Sorby-Adams, Jennifer Guo, Farrah Mateen, Philipp Goebl, Jiaming Wu, Peirong Liu, Hongwei Li, Sean I. Young, Benjamin Billot, Oula Puonti, Gordon Sze, Sam Payabavash, Adam DeHavenon, Kevin N. Sheth, Matthew S. Rosen, John Kirsch, Nicola Strisciuglio, Jelmer M. Wolterink, Arman Eshaghi, Frederik Barkhof, W. Taylor Kimberly, Juan Eugenio Iglesias
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis.
1 code implementation • 5 Dec 2023 • Sean I. Young, Yaël Balbastre, Bruce Fischl, Polina Golland, Juan Eugenio Iglesias
Here, we propose a SVR method that overcomes the shortcomings of previous work and produces state-of-the-art reconstructions in the presence of extreme inter-slice motion.
no code implementations • 2 Oct 2023 • Alan Q. Wang, Batuhan K. Karaman, Heejong Kim, Jacob Rosenthal, Rachit Saluja, Sean I. Young, Mert R. Sabuncu
To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI.
no code implementations • 24 Sep 2023 • Matthew G. French, Gonzalo D. Maso Talou, Thiranja P. Babarenda Gamage, Martyn P. Nash, Poul M. Nielsen, Anthony J. Doyle, Juan Eugenio Iglesias, Yaël Balbastre, Sean I. Young
In breast surgical planning, accurate registration of MR images across patient positions has the potential to improve the localisation of tumours during breast cancer treatment.
no code implementations • 16 Mar 2023 • Matthew A. Chan, Sean I. Young, Christopher A. Metzler
Many imaging inverse problems$\unicode{x2014}$such as image-dependent in-painting and dehazing$\unicode{x2014}$are challenging because their forward models are unknown or depend on unknown latent parameters.
1 code implementation • 4 Aug 2022 • S. Mazdak Abulnaga, Sean I. Young, Katherine Hobgood, Eileen Pan, Clinton J. Wang, P. Ellen Grant, Esra Abaci Turk, Polina Golland
In this work, we propose a machine learning model based on a U-Net neural network architecture to automatically segment the placenta in BOLD MRI and apply it to segmenting each volume in a time series.
no code implementations • 15 May 2022 • Sean I. Young, Yaël Balbastre, Adrian V. Dalca, William M. Wells, Juan Eugenio Iglesias, Bruce Fischl
In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to instead use self-supervision, with excellent results in several registration benchmarks.
no code implementations • 7 Feb 2022 • Sean I. Young, Adrian V. Dalca, Enzo Ferrante, Polina Golland, Christopher A. Metzler, Bruce Fischl, Juan Eugenio Iglesias
SUD unifies stochastic averaging and spatial denoising techniques under a spatio-temporal denoising framework and alternates denoising and model weight update steps in an optimization framework for semi-supervision.
no code implementations • 2 Sep 2020 • Sean I. Young, Wang Zhe, David Taubman, Bernd Girod
In this paper, we compress convolutional neural network (CNN) weights post-training via transform quantization.
no code implementations • CVPR 2020 • Sean I. Young, David B. Lindell, Bernd Girod, David Taubman, Gordon Wetzstein
We propose a joint albedo-normal approach to non-line-of-sight (NLOS) surface reconstruction using the directional light-cone transform (D-LCT).
no code implementations • ICCV 2019 • Sean I. Young, Aous T. Naman, Bernd Girod, David Taubman
We propose a new, filtering approach for solving a large number of regularized inverse problems commonly found in computer vision.