no code implementations • 11 Jun 2018 • Dakai Jin, Ziyue Xu, You-Bao Tang, Adam P. Harrison, Daniel J. Mollura
Qualitative results demonstrate the effectiveness of our method compared to the state-of-art.
no code implementations • 19 Mar 2018 • Dakai Jin, Ziyue Xu, Adam P. Harrison, Daniel J. Mollura
Segmentation and quantification of white matter hyperintensities (WMHs) are of great importance in studying and understanding various neurological and geriatric disorders.
no code implementations • 15 Aug 2017 • Kevin George, Adam P. Harrison, Dakai Jin, Ziyue Xu, Daniel J. Mollura
We are the first to apply deep learning to PPLS.
no code implementations • 12 Jun 2017 • Adam P. Harrison, Ziyue Xu, Kevin George, Le Lu, Ronald M. Summers, Daniel J. Mollura
Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape.
no code implementations • 19 Jan 2017 • Mingchen Gao, Ziyue Xu, Le Lu, Adam P. Harrison, Ronald M. Summers, Daniel J. Mollura
Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually delineated regions of interest (ROIs), is a clinically desirable, yet challenging goal.
no code implementations • 21 Sep 2016 • Mario Buty, Ziyue Xu, Mingchen Gao, Ulas Bagci, Aaron Wu, Daniel J. Mollura
Both sets of features were combined to estimate the nodule malignancy using a random forest classifier.
no code implementations • 11 Jul 2014 • Awais Mansoor, Ulas Bagci, Daniel J. Mollura
Low-resolution and signal-dependent noise distribution in positron emission tomography (PET) images makes denoising process an inevitable step prior to qualitative and quantitative image analysis tasks.
no code implementations • 11 Jul 2014 • Awais Mansoor, Ulas Bagci, Daniel J. Mollura
In this paper, we present a novel approach for fast, accurate, reliable segmentation of pathological lungs from CT scans by combining region-based segmentation method with local descriptor classification that is performed on an optimized sampling grid.
no code implementations • 11 Jul 2014 • Awais Mansoor, Ulas Bagci, Brent Foster, Ziyue Xu, Deborah Douglas, Jeffrey M. Solomon, Jayaram K. Udupa, Daniel J. Mollura
Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies.