Search Results for author: Jan S. Kirschke

Found 24 papers, 9 papers with code

3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images

1 code implementation15 Sep 2023 Alina F. Dima, Veronika A. Zimmer, Martin J. Menten, Hongwei Bran Li, Markus Graf, Tristan Lemke, Philipp Raffler, Robert Graf, Jan S. Kirschke, Rickmer Braren, Daniel Rueckert

In this work, we propose a novel method to segment the 3D peripancreatic arteries solely from one annotated 2D projection per training image with depth supervision.

Primitive Simultaneous Optimization of Similarity Metrics for Image Registration

no code implementations4 Apr 2023 Diana Waldmannstetter, Benedikt Wiestler, Julian Schwarting, Ivan Ezhov, Marie Metz, Daniel Rueckert, Jan S. Kirschke, Marie Piraud, Florian Kofler, Bjoern H. Menze

Even though simultaneous optimization of similarity metrics represents a standard procedure in the field of semantic segmentation, surprisingly, this does not hold true for image registration.

Image Registration Semantic Segmentation

Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations

1 code implementation27 Mar 2023 Julian McGinnis, Suprosanna Shit, Hongwei Bran Li, Vasiliki Sideri-Lampretsa, Robert Graf, Maik Dannecker, Jiazhen Pan, Nil Stolt Ansó, Mark Mühlau, Jan S. Kirschke, Daniel Rueckert, Benedikt Wiestler

Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints.


Differentiable Deconvolution for Improved Stroke Perfusion Analysis

no code implementations31 Mar 2021 Ezequiel de la Rosa, David Robben, Diana M. Sima, Jan S. Kirschke, Bjoern Menze

We show that our approach is able to generate AIFs without any manual annotation, and hence avoiding manual rater's influences.

Lesion Segmentation

AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning

no code implementations4 Oct 2020 Ezequiel de la Rosa, Diana M. Sima, Bjoern Menze, Jan S. Kirschke, David Robben

Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions.


Robustification of Segmentation Models Against Adversarial Perturbations In Medical Imaging

no code implementations23 Sep 2020 Hanwool Park, Amirhossein Bayat, Mohammad Sabokrou, Jan S. Kirschke, Bjoern H. Menze

This paper presents a novel yet efficient defense framework for segmentation models against adversarial attacks in medical imaging.

Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection

no code implementations18 Aug 2020 Malek Husseini, Anjany Sekuboyina, Maximilian Loeffler, Fernando Navarro, Bjoern H. Menze, Jan S. Kirschke

Building on state-of-art metric losses, we present a novel Grading Loss for learning representations that respect Genant's fracture grading scheme.

General Classification Representation Learning

Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis

no code implementations22 Jul 2019 Anjany Sekuboyina, Markus Rempfler, Alexander Valentinitsch, Maximilian Loeffler, Jan S. Kirschke, Bjoern H. Menze

We propose an auto-encoding network architecture for point clouds (PC) capable of extracting shape signatures without supervision.


DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis

1 code implementation29 Apr 2019 Hongwei Li, Johannes C. Paetzold, Anjany Sekuboyina, Florian Kofler, Jian-Guo Zhang, Jan S. Kirschke, Benedikt Wiestler, Bjoern Menze

Synthesizing MR imaging sequences is highly relevant in clinical practice, as single sequences are often missing or are of poor quality (e. g. due to motion).

Image Generation

A Radiomics Approach to Traumatic Brain Injury Prediction in CT Scans

no code implementations14 Nov 2018 Ezequiel de la Rosa, Diana M. Sima, Thijs Vande Vyvere, Jan S. Kirschke, Bjoern Menze

Relevant shape, intensity and texture biomarkers characterizing the different lesions are isolated and a lesion predictive model is built by using Partial Least Squares.

Decision Making Injury Prediction +2

A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets

no code implementations13 Mar 2017 Anjany Sekuboyina, Alexander Valentinitsch, Jan S. Kirschke, Bjoern H. Menze

The first stage employs a multi-layered perceptron performing non-linear regression for locating the lumbar region using the global context.

Data Augmentation

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