no code implementations • 22 Mar 2024 • Cosmin Ciausu, Deepa Krishnaswamy, Benjamin Billot, Steve Pieper, Ron Kikinis, Andrey Fedorov
Our approach shows comparable results compared to fully-supervised segmentation methods trained on MR data.
1 code implementation • 21 Dec 2023 • Benjamin Billot, Neel Dey, Daniel Moyer, Malte Hoffmann, Esra Abaci Turk, Borjan Gagoski, Ellen Grant, Polina Golland
Here we propose EquiTrack, the first method that uses recent steerable SE(3)-equivariant CNNs (E-CNN) for motion tracking.
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 • 13 Jul 2023 • Neel Dey, S. Mazdak Abulnaga, Benjamin Billot, Esra Abaci Turk, P. Ellen Grant, Adrian V. Dalca, Polina Golland
Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units.
no code implementations • 5 May 2023 • Henry F. J. Tregidgo, Sonja Soskic, Mark D. Olchanyi, Juri Althonayan, Benjamin Billot, Chiara Maffei, Polina Golland, Anastasia Yendiki, Daniel C. Alexander, Martina Bocchetta, Jonathan D. Rohrer, Juan Eugenio Iglesias
Some tools have attempted to incorporate information from diffusion MRI in the segmentation to refine these boundaries, but do not generalise well across diffusion MRI acquisitions.
1 code implementation • 5 Sep 2022 • Benjamin Billot, Colin Magdamo, You Cheng, Steven E. Arnold, Sudeshna Das, Juan. E. Iglesias
Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset.
2 code implementations • 3 Mar 2022 • Benjamin Billot, Magdamo Colin, Sean E. Arnold, Sudeshna Das, Juan. E. Iglesias
We show that this method is considerably more robust than SynthSeg, while also outperforming cascaded networks and state-of-the-art segmentation denoising methods.
no code implementations • 7 Feb 2022 • Juan Eugenio Iglesias, Riana Schleicher, Sonia Laguna, Benjamin Billot, Pamela Schaefer, Brenna McKaig, Joshua N. Goldstein, Kevin N. Sheth, Matthew S. Rosen, W. Taylor Kimberly
To address this challenge, recent advances in machine learning facilitate the synthesis of higher resolution images derived from one or multiple lower resolution scans.
2 code implementations • 20 Jul 2021 • Benjamin Billot, Douglas N. Greve, Oula Puonti, Axel Thielscher, Koen van Leemput, Bruce Fischl, Adrian V. Dalca, Juan Eugenio Iglesias
Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution.
1 code implementation • 24 Dec 2020 • Juan Eugenio Iglesias, Benjamin Billot, Yael Balbastre, Azadeh Tabari, John Conklin, Daniel C. Alexander, Polina Golland, Brian L. Edlow, Bruce Fischl
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well - typically requiring T1 scans (e. g., MP-RAGE).
no code implementations • 21 Apr 2020 • Malte Hoffmann, Benjamin Billot, Douglas N. Greve, Juan Eugenio Iglesias, Bruce Fischl, Adrian V. Dalca
This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts.
2 code implementations • 21 Apr 2020 • Benjamin Billot, Eleanor D. Robinson, Adrian V. Dalca, Juan Eugenio Iglesias
Partial voluming (PV) is arguably the last crucial unsolved problem in Bayesian segmentation of brain MRI with probabilistic atlases.
3 code implementations • MIDL 2019 • Benjamin Billot, Douglas Greve, Koen van Leemput, Bruce Fischl, Juan Eugenio Iglesias, Adrian V. Dalca
These samples are produced using the generative model of the classical Bayesian segmentation framework, with randomly sampled parameters for appearance, deformation, noise, and bias field.
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