51 citations · 51 across the 4 of their papers we have counts for
5 papers
FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation
Yunsung Chung, Chanho Lim, Chao Huang +2
Medical image segmentation of gadolinium enhancement magnetic resonance imaging (GE MRI) is an important task in clinical applications. However, manual annotation is time-consuming…
Benchmarking off-the-shelf statistical shape modeling tools in clinical applications
Anupama Goparaju, Alexandre Bone, Nan Hu +8
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technolog…
Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation
Tim Sodergren, Riddhish Bhalodia, Ross Whitaker +3
Difficult image segmentation problems, for instance left atrium MRI, can be addressed by incorporating shape priors to find solutions that are consistent with known objects. Noneth…
On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application
Anupama Goparaju, Ibolya Csecs, Alan Morris +4
Statistical shape modeling (SSM) has proven useful in many areas of biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical…
Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation
Riddhish Bhalodia, Anupama Goparaju, Tim Sodergren +6
Left atrium shape has been shown to be an independent predictor of recurrence after atrial fibrillation (AF) ablation. Shape-based representation is imperative to such an estimatio…