24 citations · 45 across the 7 of their papers we have counts for
6 papers · 1 filter
Globally Optimal Segmentation of Mutually Interacting Surfaces using Deep Learning
Hui Xie, Zhe Pan, Leixin Zhou +5
Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary and mutual influence between adjacent ob…
Unsupervised anomaly localization using VAE and beta-VAE
Leixin Zhou, Wenxiang Deng, Xiaodong Wu
Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. An VAE trained on normal images is expected to only be able to recon…
Deep Neural Networks for Surface Segmentation Meet Conditional Random Fields
Leixin Zhou, Zisha Zhong, Abhay Shah +3
Automated surface segmentation is important and challenging in many medical image analysis applications. Recent deep learning based methods have been developed for various object s…
Robust Image Segmentation Quality Assessment
Leixin Zhou, Wenxiang Deng, Xiaodong Wu
Deep learning based image segmentation methods have achieved great success, even having human-level accuracy in some applications. However, due to the black box nature of deep lear…
Optimal Multi-Object Segmentation with Novel Gradient Vector Flow Based Shape Priors
Junjie Bai, Abhay Shah, Xiaodong Wu
Shape priors have been widely utilized in medical image segmentation to improve segmentation accuracy and robustness. A major way to encode such a prior shape model is to use a mes…
Simultaneous Multiple Surface Segmentation Using Deep Learning
Abhay Shah, Michael Abramoff, Xiaodong Wu
The task of automatically segmenting 3-D surfaces representing boundaries of objects is important for quantitative analysis of volumetric images, and plays a vital role in biomedic…