180 citations · 1.3k across the 95 of their papers we have counts for
14 papers · 1 filter
Random 2.5D U-net for Fully 3D Segmentation
Christoph Angermann, Markus Haltmeier
Convolutional neural networks are state-of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge…
Sparse regularization of inverse problems by operator-adapted frame thresholding
Jürgen Frikel, Markus Haltmeier
We analyze sparse frame based regularization of inverse problems by means of a diagonal frame decomposition (DFD) for the forward operator, which generalizes the SVD. The DFD allow…
Augmented NETT Regularization of Inverse Problems
Daniel Obmann, Linh Nguyen, Johannes Schwab +1
We propose aNETT (augmented NETwork Tikhonov) regularization as a novel data-driven reconstruction framework for solving inverse problems. An encoder-decoder type network defines a…
Learned backprojection for sparse and limited view photoacoustic tomography
Johannes Schwab, Stephan Antholzer, Markus Haltmeier
Filtered backprojection (FBP) is an efficient and popular class of tomographic image reconstruction methods. In photoacoustic tomography, these algorithms are based on theoreticall…
Explicit inversion formulas for the two-dimensional wave equation from Neumann traces
Florian Dreier, Markus Haltmeier
In this article we study the problem of recovering the initial data of the two-dimensional wave equation from Neumann measurements on a convex domain with smooth boundary in the pl…
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation
Nadja Gruber, Stephan Antholzer, Werner Jaschke +2
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults, and the most common cause of death of people suffering from cirrhosis. The segmentation of…
Analysis of the Block Coordinate Descent Method for Linear Ill-Posed Problems
Simon Rabanser, Lukas Neumann, Markus Haltmeier
Block coordinate descent (BCD) methods approach optimization problems by performing gradient steps along alternating subgroups of coordinates. This is in contrast to full gradient…
Sparse synthesis regularization with deep neural networks
Daniel Obmann, Johannes Schwab, Markus Haltmeier
We propose a sparse reconstruction framework for solving inverse problems. Opposed to existing sparse regularization techniques that are based on frame representations, we train an…
Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation
Christoph Angermann, Markus Haltmeier, Ruth Steiger +2
Convolutional neural networks are state-of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge…
NETT Regularization for Compressed Sensing Photoacoustic Tomography
Stephan Antholzer, Johannens Schwab, Johannes Bauer-Marschallinger +2
We discuss several methods for image reconstruction in compressed sensing photoacoustic tomography (CS-PAT). In particular, we apply the deep learning method of [H. Li, J. Schwab,…
Deep learning versus -minimization for compressed sensing photoacoustic tomography
Stephan Antholzer, Johannes Schwab, Markus Haltmeier
We investigate compressed sensing (CS) techniques for reducing the number of measurements in photoacoustic tomography (PAT). High resolution imaging from CS data requires particula…
Photoacoustic image reconstruction via deep learning
Stephan Antholzer, Johannes Schwab, Robert Nuster +1
Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these a…
Photoacoustic image reconstruction from full field data in heterogeneous media
Markus Haltmeier, Gerhard Zangerl, Robert Nuster +1
We consider image reconstruction in full-field photoacoustic tomography, where 2D projections of the full 3D acoustic pressure distribution at a given time T>0 are collected. We di…
Deep Learning of truncated singular values for limited view photoacoustic tomography
Johannes Schwab, Stephan Antholzer, Robert Nuster +2
We develop a data-driven regularization method for the severely ill-posed problem of photoacoustic image reconstruction from limited view data. Our approach is based on the regular…