35 citations · 38 across the 6 of their papers we have counts for
6 papers
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Low-dose CT Enhancement Network with a Perceptual Loss Function in the Spatial Frequency and Image Domains
Kevin J. Chung, Roberto Souza, Richard Frayne +1
We propose a dual-domain cascade of U-nets (i.e. a "W-net") operating in both the spatial frequency and image domains to enhance low-dose CT (LDCT) images without the need for prop…
Dual-domain Cascade of U-nets for Multi-channel Magnetic Resonance Image Reconstruction
Roberto Souza, Mariana Bento, Nikita Nogovitsyn +3
The U-net is a deep-learning network model that has been used to solve a number of inverse problems. In this work, the concatenation of two-element U-nets, termed the W-net, operat…
A Hybrid Frequency-domain/Image-domain Deep Network for Magnetic Resonance Image Reconstruction
Roberto Souza, Richard Frayne
Decreasing magnetic resonance (MR) image acquisition times can potentially reduce procedural cost and make MR examinations more accessible. Compressed sensing (CS)-based image reco…
Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Consensus-based Silver standard Masks
Oeslle Lucena, Roberto Souza, Leticia Rittner +2
Convolutional neural networks (CNN) for medical imaging are constrained by the number of annotated data required in the training stage. Usually, manual annotation is considered to…
Silver Standard Masks for Data Augmentation Applied to Deep-Learning-Based Skull-Stripping
Oeslle Lucena, Roberto Souza, Letícia Rittner +2
The bottleneck of convolutional neural networks (CNN) for medical imaging is the number of annotated data required for training. Manual segmentation is considered to be the "gold-s…