44 citations · 157 across the 58 of their papers we have counts for
22 papers · 1 filter
Pitfalls of topology-aware image segmentation
Alexander H. Berger, Laurin Lux, Alexander Weers +3
Topological correctness, i.e., the preservation of structural integrity and specific characteristics of shape, is a fundamental requirement for medical imaging tasks, such as neuro…
Topograph: An efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation
Laurin Lux, Alexander H. Berger, Alexander Weers +4
Topological correctness plays a critical role in many image segmentation tasks, yet most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological…
Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings using Spine MRI data
Robert Graf, Florian Hunecke, Soeren Pohl +12
Deep learning has made significant strides in medical imaging, leveraging the use of large datasets to improve diagnostics and prognostics. However, large datasets often come with…
Towards a vision foundation model for comprehensive assessment of Cardiac MRI
Athira J Jacob, Indraneel Borgohain, Teodora Chitiboi +3
Cardiac magnetic resonance imaging (CMR), considered the gold standard for noninvasive cardiac assessment, is a diverse and complex modality requiring a wide variety of image proce…
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder
Matan Atad, David Schinz, Hendrik Moeller +6
Counterfactual explanations (CEs) aim to enhance the interpretability of machine learning models by illustrating how alterations in input features would affect the resulting predic…
Neural Network Surrogate and Projected Gradient Descent for Fast and Reliable Finite Element Model Calibration: a Case Study on an Intervertebral Disc
Matan Atad, Gabriel Gruber, Marx Ribeiro +7
Accurate calibration of finite element (FE) models is essential across various biomechanical applications, including human intervertebral discs (IVDs), to ensure their reliability…
Classification, Regression and Segmentation directly from k-Space in Cardiac MRI
Ruochen Li, Jiazhen Pan, Youxiang Zhu +2
Cardiac Magnetic Resonance Imaging (CMR) is the gold standard for diagnosing cardiovascular diseases. Clinical diagnoses predominantly rely on magnitude-only Digital Imaging and Co…
Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration
Bailiang Jian, Jiazhen Pan, Morteza Ghahremani +3
Our findings indicate that adopting "advanced" computational elements fails to significantly improve registration accuracy. Instead, well-established registration-specific designs…
Unsupervised Analysis of Alzheimer's Disease Signatures using 3D Deformable Autoencoders
Mehmet Yigit Avci, Emily Chan, Veronika Zimmer +4
With the increasing incidence of neurodegenerative diseases such as Alzheimer's Disease (AD), there is a need for further research that enhances detection and monitoring of the dis…
DCSM 2.0: Deep Conditional Shape Models for Data Efficient Segmentation
Athira J Jacob, Puneet Sharma, Daniel Rueckert
Segmentation is often the first step in many medical image analyses workflows. Deep learning approaches, while giving state-of-the-art accuracies, are data intensive and do not sca…
Learning Visual Prompts for Guiding the Attention of Vision Transformers
Razieh Rezaei, Masoud Jalili Sabet, Jindong Gu +3
Visual prompting infuses visual information into the input image to adapt models toward specific predictions and tasks. Recently, manually crafted markers such as red circles are s…
VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank
Robert Graf, Paul-Sören Platzek, Evamaria Olga Riedel +17
Objectives: To present a publicly available deep learning-based torso segmentation model that provides comprehensive voxel-wise coverage, including delineations that extend to the…
Detecting and clustering swallow events in esophageal long-term high-resolution manometry
Alexander Geiger, Lars Wagner, Daniel Rueckert +2
High-resolution manometry (HRM) is the gold standard in diagnosing esophageal motility disorders. As HRM is typically conducted under short-term laboratory settings, intermittently…
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Hartmut Häntze, Lina Xu, Christian J. Mertens +29
Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial…
Language Models Meet Anomaly Detection for Better Interpretability and Generalizability
Jun Li, Su Hwan Kim, Philip Müller +5
This research explores the integration of language models and unsupervised anomaly detection in medical imaging, addressing two key questions: (1) Can language models enhance the i…
Diffusion Models with Implicit Guidance for Medical Anomaly Detection
Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert +1
Diffusion models have advanced unsupervised anomaly detection by improving the transformation of pathological images into pseudo-healthy equivalents. Nonetheless, standard approach…
Motion-Corrected Moving Average: Including Post-Hoc Temporal Information for Improved Video Segmentation
Robert Mendel, Tobias Rueckert, Dirk Wilhelm +2
Real-time computational speed and a high degree of precision are requirements for computer-assisted interventions. Applying a segmentation network to a medical video processing tas…
Topologically Faithful Multi-class Segmentation in Medical Images
Alexander H. Berger, Nico Stucki, Laurin Lux +6
Topological accuracy in medical image segmentation is a highly important property for downstream applications such as network analysis and flow modeling in vessels or cell counting…
A Learnable Prior Improves Inverse Tumor Growth Modeling
Jonas Weidner, Ivan Ezhov, Michal Balcerak +11
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. H…
SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation
Hendrik Möller, Robert Graf, Joachim Schmitt +16
Purpose. To present SPINEPS, an open-source deep learning approach for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs,…
Towards Universal Unsupervised Anomaly Detection in Medical Imaging
Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert +1
The increasing complexity of medical imaging data underscores the need for advanced anomaly detection methods to automatically identify diverse pathologies. Current methods face ch…
On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions
Razieh Rezaei, Alireza Dizaji, Ashkan Khakzar +3
Neural networks are increasingly finding their way into the realm of graphs and modeling relationships between features. Concurrently graph neural network explanation approaches ar…