Christina Gsaxner

Christina Gsaxner
Graz University of Technology | TU Graz · Institute for Computer Graphics and Vision

Master of Science

About

69
Publications
10,667
Reads
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487
Citations
Introduction
Project Assistant at Graz University of Technology and Medical University of Graz and currently pursuing a PhD in computer science under the supervision of Prof. Dieter Schmalstieg. Current research topics include medical imaging, image analysis and augmented reality.
Additional affiliations
March 2017 - October 2017
Graz University of Technology
Position
  • Master's Student

Publications

Publications (69)
Conference Paper
Full-text available
In the treatment of head and neck cancer, physicians can benefit from augmented reality in preparing and executing treatment. We present a system allowing a physician wearing an untethered augmented reality headset to see medical visualizations precisely overlaid onto the patient. Our main contribution is a strategy for markerless registration of 3...
Conference Paper
Computer-assisted surgery is a trending topic in research, with many different approaches which aim at supporting surgeons in the operating room. Existing surgical planning and navigation solutions are often considered to be distracting, unintuitive or hard to interpret. In this work, we address this issue with an approach based on mixed reality de...
Preprint
Full-text available
The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality display, is the main player in the recent boost in medical augmented reality research. In medical settings, the HoloLens enables the physician to obtain immediate insight into patient information, directly overlaid with their view of the clinical scena...
Article
Full-text available
Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edge methods. Additionally, deep learning has delivered state-of-the-art results in tasks like autonomous driving, outclassing previous...
Preprint
Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been diffi...
Preprint
Full-text available
Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been diffi...
Conference Paper
Aortic dissection is an acute condition of the aorta. It typically starts with an intimal tear and continues with the separation of the aortic wall layers. This situation typically leads to the creation of a second lumen, i.e., the false lumen, where blood can flow into. For diagnosis of this pathology, computed tomography angiography (CTA) is usua...
Conference Paper
Cardiovascular diseases are one of the strongest burdens in healthcare. If misdiagnosed, they can lead to life-threatening complications. This is especially true for aortic dissections, which may require immediate surgery depending on the categorization and still lead to late adverse events. Aortic dissection occurs when the aortic duct splits into...
Conference Paper
This contribution presents a streamlined data pipeline to bring medical 3D scans onto Augmented Reality (AR) hardware. When a 3D scan is visualized on a 2D screen, depth information is lost and doctors have to rely on their experience to map the displayed data to the patient. Showing such a scan in AR addresses this problem, as one can view that sc...
Poster
Full-text available
This work analyses filters for vessel enhancement considering aortic dissection (AD).
Poster
Full-text available
This contribution presents a streamlined data pipeline to bring medical 3D scans onto Augmented Reality (AR) hardware.
Poster
Full-text available
This work revisits a successful modeling technique, called convolution surfaces, to visualize the three dimensional structure of aortic dissections by considering computed tomography angiography (CTA) images.
Preprint
Full-text available
Traditional convolutional neural network (CNN) methods rely on dense tensors, which makes them suboptimal for spatially sparse data. In this paper, we propose a CNN model based on sparse tensors for efficient processing of large and sparse medical images. In contrast to a dense CNN that takes the entire voxel grid as input, a sparse CNN processes o...
Preprint
Traditional convolutional neural network (CNN) methods rely on dense tensors, which makes them suboptimal for spatially sparse data. In this paper, we propose a CNN model based on sparse tensors for efficient processing of large and sparse medical images. In contrast to a dense CNN that takes the entire voxel grid as input, a sparse CNN processes o...
Preprint
Traditional convolutional neural network (CNN) methods rely on dense tensors, which makes them suboptimal for spatially sparse data. In this paper, we propose a CNN model based on sparse tensors for efficient processing of large and sparse medical images. In contrast to a dense CNN that takes the entire voxel grid as input, a sparse CNN processes o...
Article
Imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) are widely used in diagnostics, clinical studies, and treatment planning. Automatic algorithms for image analysis have thus become an invaluable tool in medicine. Examples of this are two- and three-dimensional visualizations, image segmentation, and the regist...
Data
The datasets include 56 CTA scans from mostly healthy aortas, covering the aortic arch and its branches and the abdominal aortas with the iliac arteries. Furthermore, we include segmentations (masks) of the aortas and its branches (aortic vessel trees) as binary mask images. Note: The collections includes one case with an abdominal aortic aneurysm...
Conference Paper
Surgical navigation requires tracking of instruments with respect to the patient. Conventionally, tracking is done with stationary cameras, and the navigation information is displayed on a stationary display. In contrast, an augmented reality (AR) headset can superimpose surgical navigation information directly in the surgeon’s view. However, AR ne...
Chapter
Medical images, especially volumetric images, are of high resolution and often exceed the capacity of standard desktop GPUs. As a result, most deep learning-based medical image analysis tasks require the input images to be downsampled, often substantially, before these can be fed to a neural network. However, downsampling can lead to a loss of imag...
Preprint
Full-text available
Objective: Surveillance imaging of chronic aortic diseases, such as dissections, relies on obtaining and comparing cross-sectional diameter measurements at predefined aortic landmarks, over time. Due to a lack of robust tools, the orientation of the cross-sectional planes is defined manually by highly trained operators. We show how manual annotatio...
Article
Full-text available
Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between...
Article
Full-text available
In this article, we present a skull database containing 500 healthy skulls segmented from high-resolution head computed-tomography (CT) scans and 29 defective skulls segmented from craniotomy head CTs. Each healthy skull contains the complete anatomical structures of human skulls, including the cranial bones, facial bones and other subtle structure...
Preprint
Full-text available
Medical images, especially volumetric images, are of high resolution and often exceed the capacity of standard desktop GPUs. As a result, most deep learning-based medical image analysis tasks require the input images to be downsampled, often substantially, before these can be fed to a neural network. However, downsampling can lead to a loss of imag...
Preprint
Full-text available
The aortic vessel tree is composed of the aorta and its branching arteries, and plays a key role in supplying the whole body with blood. Aortic diseases, like aneurysms or dissections, can lead to an aortic rupture, whose treatment with open surgery is highly risky. Therefore, patients commonly undergo drug treatment under constant monitoring, whic...
Article
A fast and fully automatic design of 3D printed patient-specific cranial implants is highly desired in cranioplasty - the process to restore a defect on the skull. We formulate skull defect restoration as a 3D volumetric shape completion task, where a partial skull volume is completed automatically. The difference between the completed skull and th...
Article
The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge1. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and dis...
Poster
Full-text available
Aortic dissections (AD) are injuries of the inner vessel wall of (human) aorta. As this disease poses a significant threat to a patient’s life, it is crucial to observe and analyze the progression of the dissection over the course of the disease. The examinations of the aorta are usually proceeded with the application of Computed Tomography (CT) or...
Poster
Full-text available
We extended an existing web-based tool called Studierfenster (http://studierfenster.icg.tugraz.at/) for this contribution, which was built by students from TU Graz, with a semi-automatic aortic centerline calculation functionality. Studierfenster is a tool that renders three-dimensional volumes, defined by the user, and allows the user to perform m...
Poster
Full-text available
We introduce a fully automatic system for cranial implant design, a common task in cranioplasty operations. The system is currently integrated in Studierfenster (http://studierfenster.tugraz.at/), an online, cloud-based medical image processing platform for medical imaging applications. Enhanced by deep learning algorithms, the system automatically...
Article
Full-text available
The article introduces two complementary datasets intended for the development of data-driven solutions for cranial implant design, which remains to be a time-consuming and laborious task in current clinical routine of cranioplasty. The two datasets, referred to as the SkullBreak and SkullFix in this article, are both adapted from a public head CT...
Article
Full-text available
Patient-specific craniofacial implants are used to repair skull bone defects after trauma or surgery. Currently, cranial implants are designed and produced by third-party suppliers, which is usually time-consuming and expensive. Recent advances in additive manufacturing made the in-hospital or in-operation-room fabrication of personalized implants...
Article
Background and Objective: Augmented reality (AR) can help to overcome current limitations in computer assisted head and neck surgery by granting “X-ray vision” to physicians. Still, the acceptance of AR in clinical applications is limited by technical and clinical challenges. We aim to demonstrate the benefit of a marker-free, instant calibration A...
Preprint
Full-text available
Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by mimicking the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep le...
Chapter
Aortic dissection (AD) is a condition of the main artery of the human body, resulting in the formation of a new flow channel, or false lumen (FL). The disease is usually diagnosed with a computed tomography angiography (CTA) scan during the acute phase. A better understanding of the causes of AD requires knowledge of aortic geometry prior to the ev...
Conference Paper
Augmented reality for medical applications allows physicians to obtain an inside view into the patient without surgery. In this context , we present an augmented reality application running on a standard smartphone or tablet computer, providing visualizations of medical image data, overlaid with the patient, in a video see-through fashion. Our syst...
Conference Paper
Aortic dissection (AD) is a condition of the main artery of the human body, resulting in the formation of a new flow channel, or false lumen (FL). The disease is usually diagnosed with a computed tomography angiography (CTA) scan during the acute phase. A better understanding of the causes of AD requires knowledge of aortic geometry prior to the ev...
Preprint
Full-text available
Deep learning had a remarkable impact in different scientific disciplines during the last years. This was demonstrated in numerous tasks, where deep learning algorithms were able to outperform the state-of-art methods, also in image processing and analysis. Moreover, deep learning delivers good results in tasks like autonomous driving, which could...
Chapter
In this study, we present a baseline approach for AutoImplant (https://autoimplant.grand-challenge.org/) – the cranial implant design challenge, which can be formulated as a volumetric shape learning task. In this task, the defective skull, the complete skull and the cranial implant are represented as binary voxel grids. To accomplish this task, th...
Article
Aortic dissection (AD) is a condition of the main artery of the human body, resulting in the formation of a new flow channel, or false lumen. The disease is usually diagnosed with a computed tomography angiography scan during the acute phase. A better understanding of the causes of AD requires knowledge of the aortic geometry (segmentation), includ...
Preprint
Full-text available
In this study, we present a baseline approach for AutoImplant (https://autoimplant.grand-challenge.org/) - the cranial implant design challenge, which, as suggested by the organizers, can be formulated as a volumetric shape learning task. In this task, the defective skull, the complete skull and the cranial implant are represented as binary voxel g...
Preprint
Full-text available
We introduce a fully automatic system for cranial implant design, a common task in cranioplasty operations. The system is currently integrated in Studierfenster (http://studierfenster.tugraz.at/), an online, cloud-based medical image processing platform for medical imaging applications. Enhanced by deep learning algorithms, the system automatically...
Poster
Full-text available
Given underlying computer simulations, this work provides a way to visualize Aortic Dissection (AD) in a realistic manner. Based on Virtual Reality (VR), the solution provides a visual user experience that is immersive: while conservative methods might require a researcher to sight a huge amount of medical images, VR allows the researcher to examin...
Conference Paper
Full-text available
The human organism is a highly complex system that is prone to various diseases. Some diseases are more dangerous than others, especially those that affect the circulatory system or the aorta in particular. The aorta is the largest artery in the human body. Its wall comprises several layers. When the intima, i.e. the innermost layer of the aortic w...
Conference Paper
Volumetric examinations of the aorta are nowadays of crucial importance for the management of critical pathologies such as aortic dissection, aortic aneurism, and other pathologies, which affect the morphology of the artery. These examinations usually begin with the acquisition of a Computed Tomography Angiography (CTA) scan from the patient, which...
Article
Full-text available
Medical augmented reality (AR) is an increasingly important topic in many medical fields. AR enables x-ray vision to see through real world objects. In medicine, this offers pre-, intra- or post-interventional visualization of “hidden” structures. In contrast to a classical monitor view, AR applications provide visualization not only on but also in...
Data
Medical augmented reality (AR) is an increasingly important topic in many medical fields. It enables an x-ray vision to see through real world objects. In surgery, this offers a pre-, intra- or postoperative visualization of “hidden” structures. In example, a surgeon can look through AR glasses directly at a patient while directly visualizing a pat...
Poster
Full-text available
Medical augmented reality (AR) offers a more intuitive mapping from 3D imaging to the patient, natural 3D interaction and increased perception of 3D structures, to physicians. Image-topatient registration is the key enabling technology for such AR systems. Related works use manual alignment of virtual content, marker-based registration or external...
Chapter
Full-text available
In the treatment of head and neck cancer, physicians can benefit from augmented reality in preparing and executing treatment. We present a system allowing a physician wearing an untethered augmented reality headset to see medical visualizations precisely overlaid onto the patient. Our main contribution is a strategy for markerless registration of 3...
Article
As of common routine in tumor resections, surgeons rely on local examinations of the removed tissues and on the swiftly made microscopy findings of the pathologist, which are based on intraoperatively taken tissue probes. This approach may imply an extended duration of the operation, increased effort for the medical staff, and longer occupancy of t...
Article
Background and objectives: Computer-assisted technologies, such as image-based segmentation, play an important role in the diagnosis and treatment support in cranio-maxillofacial surgery. However, although many segmentation software packages exist, their clinical in-house use is often challenging due to constrained technical, human or financial re...
Conference Paper
In this work, fully automatic binary segmentation of GBMs (glioblastoma multiforme) in 2D magnetic resonance images is presented using a convolutional neural network trained exclusively on synthetic data. The precise segmentation of brain tumors is one of the most complex and challenging tasks in clinical practice and is usually done manually by ra...
Article
Full-text available
We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especially medical image segmentation plays a vital role, since segmentation is often the initial step in an...
Poster
Full-text available
Deep learning with neural networks is an increasingly important topic for research and economic purposes. Software giants use deep networks for the development of their latest technological gadgets. Daily examples are Facebook’s face detection, Apple’s speech recognition Siri or Google Translate, which all comprise deep learning algorithms. The mo...
Conference Paper
This contribution presents the automatic segmentation of the lower jawbone (mandible) in humans’ computed tomography (CT) images with the support of trained deep learning networks. CT acquisitions from the mandible frequently include radiological artifacts e.g. from metal dental restorations, ostheosynthesis materials or include trauma related free...
Conference Paper
Full-text available
Accurate segmentation and measurement of brain tumors plays an important role in clinical practice and research, as it is critical for treatment planning and monitoring of tumor growth. However, brain tumor segmentation is one of the most challenging tasks in medical image analysis. Since manual segmentations are subjective, time consuming and neit...
Poster
Full-text available
Accurate segmentation and measurement of brain tumors plays an important role in clinical practice and research, as it is critical for treatment planning and monitoring of tumor growth. However, brain tumor segmentation is one of the most challenging tasks in medical image analysis. Since manual segmentations are subjective, time consuming and neit...
Conference Paper
Accurate segmentation of medical images is a key step in medical image processing. As the amount of medical images obtained in diagnostics, clinical studies and treatment planning increases, automatic segmentation algorithms become increasingly more important. Therefore, we plan to develop an automatic segmentation approach for the urinary bladder...
Conference Paper
Full-text available
The lower jawbone data preparation for deep learning is proposed. In ten cases, surgeons segmented the lower jawbone in each slice to generate the ground truth. Since the number of present images was deemed insufficient to train a deep neural network, data was augmented with geometric transformations and added noise. Flipping, rotating and scaling...
Poster
Full-text available
Segmentation is an important branch in medical image processing and the basis for further detailed investigations on computed tomography (CT), magnetic resonance imaging (MRI), X-ray, ultrasound (US) or nuclear images. Through segmentation, an image is divided into various connected areas that correspond to certain tissue types. A common aim is to...
Poster
Full-text available
Accurate segmentation of medical images is a key step in medical image processing. As the amount of medical images obtained in diagnostics, clinical studies and treatment planning increases, automatic segmentation algorithms become increasingly more important. Therefore, we plan to develop an automatic segmentation approach for the urinary bladder...

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