7 papers
Schrödinger-Inspired Time-Evolution for 4D Deformation Forecasting
Ahsan Raza Siyal, Markus Haltmeier, Ruth Steiger +2
Spatiotemporal forecasting of complex three-dimensional phenomena (4D: 3D + time) is fundamental to applications in medical imaging, fluid and material dynamics, and geophysics. In…
Robust Rigid and Non-Rigid Medical Image Registration Using Learnable Edge Kernels
Ahsan Raza Siyal, Markus Haltmeier, Ruth Steiger +3
Medical image registration is crucial for various clinical and research applications including disease diagnosis or treatment planning which require alignment of images from differ…
DARE: A Deformable Adaptive Regularization Estimator for Learning-Based Medical Image Registration
Ahsan Raza Siyal, Markus Haltmeier, Ruth Steiger +3
Deformable medical image registration is a fundamental task in medical image analysis. While deep learning-based methods have demonstrated superior accuracy and computational effic…
Error Estimates for Data-driven Weakly Convex Frame-based Image Regularization
Andrea Ebner, Matthias Schwab, Markus Haltmeier
Inverse problems are fundamental in fields like medical imaging, geophysics, and computerized tomography, aiming to recover unknown quantities from observed data. However, these pr…
A lightweight residual network for unsupervised deformable image registration
Ahsan Raza Siyal, Astrid Ellen Grams, Markus Haltmeier
Accurate volumetric image registration is highly relevant for clinical routines and computer-aided medical diagnosis. Recently, researchers have begun to use transformers in learni…
Deep Gaussian mixture model for unsupervised image segmentation
Matthias Schwab, Agnes Mayr, Markus Haltmeier
The recent emergence of deep learning has led to a great deal of work on designing supervised deep semantic segmentation algorithms. As in many tasks sufficient pixel-level labels…
Three-dimensional Bone Image Synthesis with Generative Adversarial Networks
Christoph Angermann, Johannes Bereiter-Payr, Kerstin Stock +2
Medical image processing has been highlighted as an area where deep learning-based models have the greatest potential. However, in the medical field in particular, problems of data…