1 code implementation • 10 Mar 2024 • Peirong Liu, Oula Puonti, Annabel Sorby-Adams, William T. Kimberly, Juan E. Iglesias
Remarkable progress has been made by data-driven machine-learning methods in the analysis of MRI scans.
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 • 28 Nov 2023 • Peirong Liu, Oula Puonti, Xiaoling Hu, Daniel C. Alexander, Juan E. Iglesias
We present new metrics to validate the intra- and inter-subject robustness of Brain-ID features, and evaluate their performance on four downstream applications, covering contrast-independent (anatomy reconstruction/contrast synthesis, brain segmentation), and contrast-dependent (super-resolution, bias field estimation) tasks.
no code implementations • 20 Nov 2022 • Peirong Liu, Rui Wang, Pengchuan Zhang, Omid Poursaeed, Yipin Zhou, Xuefei Cao, Sreya Dutta Roy, Ashish Shah, Ser-Nam Lim
We propose TrIVD (Tracking and Image-Video Detection), the first framework that unifies image OD, video OD, and MOT within one end-to-end model.
no code implementations • 16 Mar 2022 • Maxime Oquab, Daniel Haziza, Ludovic Schwartz, Tao Xu, Katayoun Zand, Rui Wang, Peirong Liu, Camille Couprie
As the quality of few shot facial animation from landmarks increases, new applications become possible, such as ultra low bandwidth video chat compression with a high degree of realism.
1 code implementation • 6 Mar 2022 • Lin Tian, Connor Puett, Peirong Liu, Zhengyang Shen, Stephen R. Aylward, Yueh Z. Lee, Marc Niethammer
We demonstrate our approach for the registration between CT and stationary chest tomosynthesis (sDCT) images and show how it naturally leads to an iterative image reconstruction approach.
no code implementations • CVPR 2022 • Peirong Liu, Yueh Lee, Stephen Aylward, Marc Niethammer
Extensive comparisons demonstrate that our model successfully distinguishes stroke lesions (abnormal) from normal brain regions, while reconstructing the underlying velocity and diffusion tensor fields.
2 code implementations • NeurIPS 2021 • Zhengyang Shen, Jean Feydy, Peirong Liu, Ariel Hernán Curiale, Ruben San Jose Estepar, Raul San Jose Estepar, Marc Niethammer
Finally, we showcase the performance of transport-enhanced registration models on a wide range of challenging tasks: rigid registration for partial shapes; scene flow estimation on the Kitti dataset; and nonparametric registration of lung vascular trees between inspiration and expiration.
no code implementations • 9 Oct 2021 • Peirong Liu, Rui Wang, Xuefei Cao, Yipin Zhou, Ashish Shah, Ser-Nam Lim
Key findings are twofold: (1) by capturing the motion transfer with an ordinary differential equation (ODE), it helps to regularize the motion field, and (2) by utilizing the source image itself, we are able to inpaint occluded/missing regions arising from large motion changes.
no code implementations • CVPR 2021 • Peirong Liu, Lin Tian, Yubo Zhang, Stephen R. Aylward, Yueh Z. Lee, Marc Niethammer
To help with identifiability, we develop an advection-diffusion simulator which allows pre-training of our model by supervised learning using the velocity and diffusion tensor fields.
1 code implementation • 6 Sep 2020 • Peirong Liu, Yueh Z. Lee, Stephen R. Aylward, Marc Niethammer
In this work we therefore propose a data-assimilation approach (PIANO) which estimates the velocity and diffusion fields of an advection-diffusion model that best explains the contrast dynamics.
1 code implementation • 6 Sep 2020 • Peirong Liu, Zhengwang Wu, Gang Li, Pew-Thian Yap, Dinggang Shen
Charting cortical growth trajectories is of paramount importance for understanding brain development.
1 code implementation • ICCV 2021 • Zhipeng Ding, Xu Han, Peirong Liu, Marc Niethammer
Thus, we propose a learning-based calibration method that focuses on multi-label semantic segmentation.