Abstract:
Current and ongoing work. We aim to create a reduced approach that exploits technologies from machine learning (e.g., neural networks and auto-encoder networks) and dimensionality reduction models (e.g., low-rank and latent representations) to advance various technologies for inverse problems. We consider a decoupled approach for surrogate modeling, where unsupervised learning approaches are used to efficiently represent the input and target spaces separately, and a supervised learning approach is used to represent the mapping from one latent space to another. We have demonstrated that our approach can outperform others in scenarios where training data for unsupervised learning is easily available, but the number of input/target pairs for supervised learning is small, and we introduce how the approach can be used for defining regularization or prior knowledge, and/or as a surrogate model for inversion, forward propagation, and adjoint free methods.
Download:
Comparison of Atlas-Based and Neural-Network-Based Semantic Segmentation for DENSE MRI Images
(joint with Lars Ruthotto, Elle Buser, and Ben Huenemann)
(joint with Lars Ruthotto, Elle Buser, and Ben Huenemann)
Abstract:
Two segmentation methods, one atlas-based and one neural-network-based, were compared to see how well they can each automatically segment the brain stem and cerebellum in Displacement Encoding with Stimulated Echoes Magnetic Resonance Imaging (DENSE-MRI) data. The segmentation is a pre-requisite for estimating the average displacements in these regions, which have recently been proposed as biomarkers in the diagnosis of Chiari Malformation type I (CMI). In numerical experiments, the segmentations of both methods were similar to manual segmentations provided by trained experts. It was found that, overall, the neural-network-based method alone produced more accurate segmentations than the atlas-based method did alone, but that a combination of the two methods, in which the atlas-based method is used for the segmentation of the brain stem and the neural-network is used for the segmentation of the cerebellum, may be the most successful.
Download: