Darko ŠternMedical University of Graz · Institute of Biophysics
Darko Štern
PhD
About
61
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1,895
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Citations since 2017
Introduction
Additional affiliations
May 2015 - present
May 2013 - May 2015
May 2012 - May 2013
Publications
Publications (61)
Even though many semantic segmentation methods exist that are able to perform well on many medical datasets, often, they are not designed for direct use in clinical practice. The two main concerns are generalization to unseen data with a different visual appearance, e.g., images acquired using a different scanner, and efficiency in terms of computa...
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine...
In landmark localization, due to ambiguities in defining their exact position, landmark annotations may suffer from large observer variabilities, which result in uncertain annotations. To model the annotation ambiguities of the training dataset, we propose to learn anisotropic Gaussian parameters modeling the shape of the target heatmap during opti...
Brain ageing is a complex neurobiological process associated with morphological changes that can be assessed on MRI scans. Recently, Deep learning (DL)-based approaches have been proposed for the prediction of chronological brain age from MR images yielding high accuracy. These approaches, however, usually do not address quantification of uncertain...
Cardiac digital twins (Cardiac Digital Twin (CDT)s) of human electrophysiology (Electrophysiology (EP)) are digital replicas of patient hearts derived from clinical data that match like-for-like all available clinical observations. Due to their inherent predictive potential, CDTs show high promise as a complementary modality aiding in clinical deci...
We propose a reinforcement learning (RL) based approach for anatomical landmark localization in medical images, where the agent can move in arbitrary directions with a variable step size. Using a continuous action space reduces the average number of steps required to locate a landmark by more than 30 times compared to localization using discrete ac...
In landmark localization, due to ambiguities in defining their exact position, landmark annotations may suffer from both large inter- and intra-observer variabilites, which result in uncertain annotations. Therefore, predicting a single coordinate for a landmark is not sufficient for modeling the distribution of possible landmark locations. We prop...
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural weight bearing is crucial for such bio-mechanical analysis. 3D volumetric imaging modalities (e.g. CT and MRI) are performed in patients lying down. On the other hand, ra...
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural weight bearing is crucial for such bio-mechanical analysis. 3D volumetric imaging modalities (e.g. CT and MRI) are performed in patients lying down. On the other hand, ra...
Additionally to the extensive use in clinical medicine, biological age (BA) in legal medicine is used to assess unknown chronological age (CA) in applications where identification documents are not available. Automatic methods for age estimation proposed in the literature are predicting point estimates, which can be misleading without the quantific...
Despite some design limitations, CNNs have been largely adopted by the computer vision community due to their efficacy and versatility. Introduced by Sabour et al. to circumvent some limitations of CNNs, capsules replace scalars with vectors to encode appearance feature representation, allowing better preservation of spatial relationships between w...
In many medical image analysis applications, often only a limited amount of training data is available, which makes training of convolutional neural networks (CNNs) challenging. In this work on anatomical landmark localization, we propose a CNN architecture that learns to split the localization task into two simpler sub-problems, reducing the need...
Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart sha...
Highly relevant for both clinical and legal medicine applications, the established radiological methods for estimating unknown age in children and adolescents are based on visual examination of bone ossification in X-ray images of the hand. Our group has initiated the development of fully automatic age estimation methods from 3D MRI scans of the ha...
Differently to semantic segmentation, instance segmentation assigns unique labels to each individual instance of the same object class. In this work, we propose a novel recurrent fully convolutional network architecture for tracking such instance segmentations over time, which is highly relevant, e.g., in biomedical applications involving cell grow...
In many medical image analysis applications, only a limited amount of training data is available due to the costs of image acquisition and the large manual annotation effort required from experts. Training recent state-of-the-art machine learning methods like convolutional neural networks (CNNs) from small datasets is a challenging task. In this wo...
Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape,...
Age estimation from radiologic data is an important topic both in clinical medicine as well as in forensic applications, where it is used to assess unknown chronological age or to discriminate minors from adults. In this work, we propose an automatic multi-factorial age estimation method based on MRI data of hand, clavicle and teeth to extend the m...
Different to semantic segmentation, instance segmentation assigns unique labels to each individual instance of the same class. In this work, we propose a novel recurrent fully convolutional network architecture for tracking such instance segmentations over time. The network architecture incorporates convolutional gated recurrent units (ConvGRU) int...
We propose a 2D computed tomography (CT) slice image reconstruction method from a limited number of projection images using Wasserstein generative adversarial networks (wGAN). Our wGAN optimizes the 2D CT image reconstruction by utilizing an adversarial loss to improve the perceived image quality as well as an \(L_1\) content loss to enforce struct...
Different to semantic segmentation, instance segmentation assigns unique labels to each individual instance of the same class. In this work, we propose a novel recurrent fully convolutional network architecture for tracking such instance segmentations over time. The network architecture incorporates convolutional gated recurrent units (ConvGRU) int...
In recent years, deep learning based methods achieved state-of-the-art performance in many computer vision tasks. However, these methods are typically supervised, and require large amounts of annotated data to train. Acquisition of annotated data can be a costly endeavor, especially for methods requiring pixel-wise annotations such as image segment...
We propose a method for 3D computed tomography (CT) image reconstruction from 3D digitally reconstructed radiographs (DRR). The 3D DRR images are generated from 2D projection images of the 3D CT image from different angles and used to train a convolutional neural network (CNN). Evaluating with a different number of input DRR images, we compare our...
We propose a two component fully-convolutional network for heatmap regression to perform multi-person pose estimation from images. The first component of the network predicts all body joints of all persons visible on an image , while the second component groups these body joints based on the position of the head of the person of interest. By applyi...
Age estimation from radiologic data is an important topic in forensic medicine to assess chronological age or to discriminate minors from adults, e.g. asylum seekers lacking valid identification documents. In this work we propose automatic multi-factorial age estimation methods based on MRI data to extend the maximal age range from 19 years, as com...
In approaches for automatic localization of multiple anatomical landmarks, disambiguation of locally similar structures as obtained by locally accurate candidate generation is often performed by solely including high level knowledge about geometric landmark configuration. In our novel localization approach, we propose to combine both image appearan...
Modern deep learning methods achieve state-of-the-art results in many computer vision tasks. While these methods perform well when trained on large datasets, deep learning methods suffer from overfitting and lack of generalization given smaller datasets. Especially in medical image analysis, acquisition of both imaging data and corresponding ground...
Die vorliegende Arbeit bietet einen aktuellen Überblick über die Altersschätzung an lebenden Personen. Dabei wird nach einer generellen Einleitung über die Methoden der Altersdiagnostik im Speziellen, die von der AGFAD empfohlene multi-faktorielle Untersuchungsmethodik betrachtet, welche neben einer äußerlichen, körperlichen Untersuchung auf der Sc...
Biological age (BA) estimation from radiologic data is an important topic in clinical medicine, e.g. in determining endocrinologi-cal diseases or planning paediatric orthopaedic surgeries, while in legal medicine it is employed to approximate chronological age. In this work, we propose the use of deep convolutional neural networks (DCNN) for automa...
We explore the applicability of deep convolutional neural networks (CNNs) for multiple landmark localization in medical image data. Exploiting the idea of regressing heatmaps for individual landmark locations, we investigate several fully convolutional 2D and 3D CNN ar-chitectures by training them in an end-to-end manner. We further propose a novel...
State of the art anatomical landmark localization algorithms pair local Random Forest (RF) detection with disambiguation of locally similar structures by including high level knowledge about relative landmark locations. In this work we pursue the question, how much high-level knowledge is needed in addition to a single landmark localization RF to i...
The evaluation of changes in Intervertebral Discs (IVDs) with 3D Magnetic Resonance (MR) Imaging (MRI) can be of interest for many clinical applications. This paper presents the evaluation of both IVD localization and IVD segmentation methods submitted to the Automatic 3D MRI IVD Localiza tion and Segmentation challenge, held at the 2015 Internatio...
We introduce a fully automatic localization and segmentation pipeline for three-dimensional (3D) intervertebral discs (IVDs), consisting of a regression-based prediction of vertebral bodies and IVD positions as well as a 3D geodesic active contour segmentation delineating the IVDs. The approach was evaluated on the data set of the challenge in conj...
Selection of set of training pixels and feature range show to be critical scale-related parameters with high impact on results in localization methods based on random regression forests (RRF). Trained on pixels randomly selected from images with long range features, RRF captures the variation in landmark location but often without reaching satisfyi...
Increasingly important for both clinical and forensic medicine, radiological age estimation is performed by fusing independent bone age estimates from hand images. In this work, we show that the artificial separation into bone independent age estimates as used in established fusion techniques can be overcome. Thus, we treat aging as a global develo...
Age estimation of individuals is important in human biology and has various medical and forensic applications. Recent interest in MR-based methods aims to investigate alternatives for established methods involving ionising radiation. Automatic, software-based methods additionally promise improved estimation objectivity.
To investigate how informati...
Radiological age estimation of living subjects from MR images has recently become very popular. Besides skeletal ossification this can be done using the mineralization status of wisdom teeth. To support potential automatic age estimation, an important preliminary step is a reliable and automatic localization of the wisdom teeth. Therefore, we propo...
Automatic segmentation of 3D vertebrae is a challenging task in medical imaging. In this paper, we introduce a total variation (TV) based framework that incorporates an a priori model, i.e., a vertebral mean shape, image intensity and edge information. The algorithm was evaluated using leave-one-out cross validation on a data set containing ten com...
Automatic segmentation of 3D vertebrae is a challenging task in medical imaging. In this paper, we introduce a total variation (TV) based framework that incorporates an a priori model, i.e., a vertebral mean shape, image intensity and edge information. The algorithm was evaluated using leave-one-out cross validation on a data set containing ten com...
Percutaneous vertebroplasty is a widely used vertebral augmentation technique. It is a minimally invasive and low-risk procedure, but has some disadvantages with a relatively high number of bone cement leaks and adjacent vertebral fractures. The aim of this cadaveric study was to determine the minimum percentage of cement fill volume in vertebropla...
There has recently been an increased demand in bone age estimation (BAE) of living individuals and human remains in legal medicine applications. A severe drawback of established BAE techniques based on X-ray images is radiation exposure, since many countries prohibit scanning involving ionizing radiation without diagnostic reasons. We propose a com...
Bone age estimation (BAE) is an important procedure in forensic practice which recently has seen a shift in attention from X-ray to MRI based imaging. To automate BAE from MRI, localization of the joints between hand bones is a crucial first step, which is challenging due to anatomical variations, different poses and repeating structures within the...
The determination of an individual’s legal majority age is becoming increasingly important in forensic practice. Established age estimation methods are based on 2D X-rays, but suffer from problems due to projective imaging and exposure to ionizing radiation, which, without proper medical or criminal indication, is ethically questionable and legally...
Degenerative changes of the intervertebral disc are among the most common causes of low back pain, where for individuals with significant symptoms surgery may be needed. One of the interventions is the total disc replacement surgery, where the degenerated disc is replaced by an artificial implant. For designing implants with good bone contact and c...
Identification of vertebral deformations in two dimensions (2D) is a challenging task due to the projective nature
of radiographic images and natural anatomical variability of vertebrae. By generating detailed three-dimensional
(3D) anatomical images, computed tomography (CT) enables accurate measurement of vertebral deformations.
We present a nove...
Unlabelled:
Quantitative vertebral morphometry (QVM) was performed by parametric modeling of vertebral bodies in three dimensions (3D).
Introduction:
Identification of vertebral fractures in two dimensions is a challenging task due to the projective nature of radiographic images and variability in the vertebral shape. By generating detailed 3D a...
Endovascular treatment of cerebral aneurysms and arteriovenous
malformations (AVM) involves navigation of a catheter through the
femoral artery and vascular system to the site of pathology.
Intra-interventional navigation is done under the guidance of one or at
most two two-dimensional (2D) X-ray fluoroscopic images or 2D digital
subtracted angiogr...
Accurate and objective evaluation of vertebral body deformations
represents an important part of the clinical diagnostics and therapy of
pathological conditions affecting the spine. Although modern clinical
practice is oriented towards threedimensional (3D) imaging techniques,
the established methods for the evaluation of vertebral body
deformation...
Accurate and objective evaluation of vertebral deformations is of significant importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is focused on three-dimensional (3D) computed tomography (CT) and magnetic resonance (MR) imaging techniques, the established methods for evalu...
The evaluation of vertebral deformations is of great importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is oriented towards the computed tomography (CT) and magnetic resonance (MR) imaging techniques, as they can provide a detailed D representation of vertebrae, the estab...
Segmentation of vertebrae provides means for reliable measurement of vertebral deformations, which is important for the diagnosis
and therapy of pathological conditions affecting the spine. In this paper we propose a method for segmentation of vertebral
bodies in three-dimensional (3D) magnetic resonance (MR) images that is based on efficient geome...
The knowledge of the location of the centers of vertebral bodies and intervertebral discs is valuable for the analysis of the spine. Existing methods for the detection and segmentation of vertebrae in images acquired by computed tomography (CT) and magnetic resonance (MR) imaging are usually applicable only to a specific image modality and require...
We propose a completely automated algorithm for the detection of the spinal centreline and the centres of vertebral bodies and intervertebral discs in images acquired by computed tomography (CT) and magnetic resonance (MR) imaging. The developed methods are based on the analysis of the geometry of spinal structures and the characteristics of CT and...
The spinal curvature is one of the most important parameters for the evaluation of spinal deformities. The spinal centerline, represented by the curve that passes through the centers of the vertebral bodies in three-dimensions (3D), allows valid quantitative measurements of the spinal curvature at any location along the spine. We propose a novel au...
Innovations in motors and motor drive systems, that could replace "universal motor" brush machines in residential applications, bring adjustable speed motor system costing less than US $40 for a 500W unit. The goal of this paper is to present use of flyback converter for auxiliary power supply in a proposed integrated electrical drive. Paper also s...
Projects
Project (1)
Radiation free age estimation of youths and young adolescents is a topic of rising interest recently. In this project we investigate automatic algorithms which enable based on computer vision & machine learning to create a nonlinear function from a training database of MR images with the corresponding chronological age, that is able to predict the age of novel, previously unseen data sets.
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