4 citations · 6 across the 5 of their papers we have counts for
5 papers
Learning to segment with limited annotations: Self-supervised pretraining with regression and contrastive loss in MRI
Lavanya Umapathy, Zhiyang Fu, Rohit Philip +3
Obtaining manual annotations for large datasets for supervised training of deep learning (DL) models is challenging. The availability of large unlabeled datasets compared to labele…
A Cascaded Residual UNET for Fully Automated Segmentation of Prostate and Peripheral Zone in T2-weighted 3D Fast Spin Echo Images
Lavanya Umapathy, Wyatt Unger, Faryal Shareef +4
Multi-parametric MR images have been shown to be effective in the non-invasive diagnosis of prostate cancer. Automated segmentation of the prostate eliminates the need for manual a…
White matter hyperintensities volume and cognition: Assessment of a deep learning based lesion detection and quantification algorithm on the Alzheimers Disease Neuroimaging Initiative
Lavanya Umapathy, Gloria Guzman Perez-Carillo, Blair Winegar +3
The relationship between cognition and white matter hyperintensities (WMH) volumes often depends on the accuracy of the lesion segmentation algorithm used. As such, accurate detect…
A Comparison of Deep Learning Convolution Neural Networks for Liver Segmentation in Radial Turbo Spin Echo Images
Lavanya Umapathy, Mahesh Bharath Keerthivasan, Jean-Phillipe Galons +4
Motion-robust 2D Radial Turbo Spin Echo (RADTSE) pulse sequence can provide a high-resolution composite image, T2-weighted images at multiple echo times (TEs), and a quantitative T…
Quantitative T2 Estimation Using Radial Turbo Spin Echo Imaging
Mahesh B Keerthivasan, Ali Bilgin, Maria I Altbach
There has been increased interest in the quantitative characterization of tissues based on T2 in abdominal imaging. Techniques based on spin-echo or turbo spin-echo sequences are t…