Search Results for author: Bjoern H. Menze

Found 34 papers, 6 papers with code

A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning

no code implementations1 Mar 2022 Fernando Navarro, Guido Sasahara, Suprosanna Shit, Ivan Ezhov, Jan C. Peeken, Stephanie E. Combs, Bjoern H. Menze

Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning.

A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images

no code implementations24 Oct 2021 Giles Tetteh, Fernando Navarro, Johannes Paetzold, Jan Kirschke, Claus Zimmer, Bjoern H. Menze

First, it is time-consuming - the clinician needs to scan through several slices of images to ascertain the region of interest before deciding on what severity grade to assign to a patient.


Semi-Implicit Neural Solver for Time-dependent Partial Differential Equations

no code implementations3 Sep 2021 Suprosanna Shit, Ivan Ezhov, Leon Mächler, Abinav R., Jana Lipkova, Johannes C. Paetzold, Florian Kofler, Marie Piraud, Bjoern H. Menze

In this paper, we propose a neural solver to learn an optimal iterative scheme in a data-driven fashion for any class of PDEs.

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)

1 code implementation30 Aug 2021 Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar, Mihail I. Todorov, Anjany Sekuboyina, Georgios Kaissis, Ali Ertürk, Stephan Günnemann, Bjoern H. Menze

Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset.

Graph Learning

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

Deep Reinforcement Learning for Organ Localization in CT

no code implementations MIDL 2019 Fernando Navarro, Anjany Sekuboyina, Diana Waldmannstetter, Jan C. Peeken, Stephanie E. Combs, Bjoern H. Menze

Robust localization of organs in computed tomography scans is a constant pre-processing requirement for organ-specific image retrieval, radiotherapy planning, and interventional image analysis.

Image Retrieval reinforcement-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.

Cross-view Relation Networks for Mammogram Mass Detection

no code implementations1 Jul 2019 Jiechao Ma, Sen Liang, Xiang Li, Hongwei Li, Bjoern H. Menze, Rongguo Zhang, Wei-Shi Zheng

Mammogram is the most effective imaging modality for the mass lesion detection of breast cancer at the early stage.

Lesion Detection

Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images

no code implementations18 Dec 2018 Jiechao Ma, Xiang Li, Hongwei Li, Bjoern H. Menze, Sen Liang, Rongguo Zhang, Wei-Shi Zheng

In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group convolution on 3D single-shot detector (SSD) called group-attention SSD (GA-SSD).

Computed Tomography (CT) Finding Pulmonary Nodules In Large-Scale Ct Images

Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function

no code implementations22 Oct 2018 Xiaobin Hu, Hongwei Li, Yu Zhao, Chao Dong, Bjoern H. Menze, Marie Piraud

Based on the same start-of-the-art network architecture, the accuracy of nested-class (enhancing tumor) is reasonably improved from 69% to 72% compared with the traditional Softmax-based method which blind to topological prior.

Brain Tumor Segmentation Tumor Segmentation

Qunatification of Metabolites in MR Spectroscopic Imaging using Machine Learning

no code implementations25 May 2018 Dhritiman Das, Eduardo Coello, Rolf F Schulte, Bjoern H. Menze

The goal of our proposed framework is to learn the spectral features from a training set comprising of different variations of both simulated and in-vivo brain spectra and then use this learning for the subsequent metabolite quantification.

Direct Estimation of Pharmacokinetic Parameters from DCE-MRI using Deep CNN with Forward Physical Model Loss

no code implementations8 Apr 2018 Cagdas Ulas, Giles Tetteh, Michael J. Thrippleton, Paul A. Armitage, Stephen D. Makin, Joanna M. Wardlaw, Mike E. Davies, Bjoern H. Menze

Dynamic contrast-enhanced (DCE) MRI is an evolving imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters in body tissues, in which series of T1-weighted images are collected following the administration of a paramagnetic contrast agent.

Time Series

DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual Learning

1 code implementation8 Apr 2018 Cagdas Ulas, Giles Tetteh, Stephan Kaczmarz, Christine Preibisch, Bjoern H. Menze

Arterial spin labeling (ASL) allows to quantify the cerebral blood flow (CBF) by magnetic labeling of the arterial blood water.


Multi-level Activation for Segmentation of Hierarchically-nested Classes

no code implementations5 Apr 2018 Marie Piraud, Anjany Sekuboyina, Bjoern H. Menze

For many biological image segmentation tasks, including topological knowledge, such as the nesting of classes, can greatly improve results.

Multi-class Classification Semantic Segmentation

DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes

no code implementations25 Mar 2018 Giles Tetteh, Velizar Efremov, Nils D. Forkert, Matthias Schneider, Jan Kirschke, Bruno Weber, Claus Zimmer, Marie Piraud, Bjoern H. Menze

Our experiments show that, by replacing 3-D filters with cross-hair filters in our network, we achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy (with a Cox-Wilcoxon paired sample significance test p-value of 0. 07 when compared to full 3-D filters).

Transfer Learning

Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction

no code implementations12 Apr 2017 Giles Tetteh, Markus Rempfler, Bjoern H. Menze, Claus Zimmer

Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling.

Classification General Classification

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

Efficient Algorithms for Moral Lineage Tracing

no code implementations ICCV 2017 Markus Rempfler, Jan-Hendrik Lange, Florian Jug, Corinna Blasse, Eugene W. Myers, Bjoern H. Menze, Bjoern Andres

Lineage tracing, the joint segmentation and tracking of living cells as they move and divide in a sequence of light microscopy images, is a challenging task.

The Minimum Cost Connected Subgraph Problem in Medical Image Analysis

no code implementations20 Jun 2016 Markus Rempfler, Bjoern Andres, Bjoern H. Menze

Several important tasks in medical image analysis can be stated in the form of an optimization problem whose feasible solutions are connected subgraphs.

Cannot find the paper you are looking for? You can Submit a new open access paper.