Multi-modality examinations have been extensively applied in current clinical cancer management. Leveraging multi-modality medical images can be highly beneficial for automated tumor segmentation as they provide complementary information that could m...
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Jan 7, 2019
BACKGROUND: Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning ha...
Deriving accurate attenuation maps for PET/MRI remains a challenging problem because MRI voxel intensities are not related to properties of photon attenuation and bone/air interfaces have similarly low signal. This work presents a learning-based meth...
Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found. An inexpensive, noninvasive screening tool for ALVD in the doctor's ...
Neural networks : the official journal of the International Neural Network Society
Jan 6, 2019
Parkinson's disease (PD) is a common neurodegenerative disorder that affects human's quality of life, especially leading to locomotor deficits such as postural instability and gait disturbances. Gait signal is one of the best features to characterize...
Robot-assisted surgery (RAS) requires a large capital investment by healthcare organizations. The cost of a robotic unit is fixed, so institutions must maximize use of each unit by utilizing all available operating room block time. One way to increas...
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jan 4, 2019
Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to the lack of feedback from physical therapists (PTs). In this paper, we analyze the feasibility of using trunk sway data and machine learning (ML) t...
OBJECTIVE: To develop different radiomic models based on the magnetic resonance imaging (MRI) radiomic features and machine learning methods to predict early intensity-modulated radiation therapy (IMRT) response, Gleason scores (GS) and prostate canc...
AJR. American journal of roentgenology
Jan 2, 2019
OBJECTIVE: The purpose of this study is to evaluate the potential value of machine learning (ML)-based high-dimensional quantitative CT texture analysis in predicting the mutation status of the gene encoding the protein polybromo-1 (PBRM1) in patient...
IEEE journal of biomedical and health informatics
Jan 1, 2019
Automated tissue classification is an essential step for quantitative analysis and treatment of emphysema. Although many studies have been conducted in this area, there still remain two major challenges. First, different emphysematous tissue appears ...
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