activity
20172026
most citedLearning MRI Artifact Removal With Unpaired Data

50 citations · 111 across the 14 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2021

A Self-Supervised Deep Framework for Reference Bony Shape Estimation in Orthognathic Surgical Planning

Deqiang Xiao, Hannah Deng, Tianshu Kuang +11

Virtual orthognathic surgical planning involves simulating surgical corrections of jaw deformities on 3D facial bony shape models. Due to the lack of necessary guidance, the planni…

cs.CV2020

Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces

Peirong Liu, Zhengwang Wu, Gang Li +2

Charting cortical growth trajectories is of paramount importance for understanding brain development. However, such analysis necessitates the collection of longitudinal data, which…

cs.CV20207 cited

An Auto-Context Deformable Registration Network for Infant Brain MRI

Dongming Wei, Sahar Ahmad, Yunzhi Huang +7

Deformable image registration is fundamental to longitudinal and population analysis. Geometric alignment of the infant brain MR images is challenging, owing to rapid changes in im…

cs.CV2019

Multi-Kernel Filtering for Nonstationary Noise: An Extension of Bilateral Filtering Using Image Context

Feihong Liu, Jun Feng, Pew-Thian Yap +1

Bilateral filtering (BF) is one of the most classical denoising filters, however, the manually initialized filtering kernel hampers its adaptivity across images with various charac…

cs.CV2019

Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks

Siyuan Liu, Kim-Han Thung, Weili Lin +2

In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images. IQA is first performed slice-wise using a nonlocal residual neural…

cs.CV2018

BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks

Jingfan Fan, Xiaohuan Cao, Pew-Thian Yap +1

In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for train…