Christina Gsaxner

• Master of Science
• PhD Student at Graz University of Technology

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...

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...

Objectives The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). H...

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image...

This paper addresses the growing integration of Augmented Reality (AR) in biomedical sciences, emphasizing collaborative learning experiences. We present MultiAR, a versatile , domain-specific platform enabling multiuser interactions in AR for biomedical education. Unlike platform-specific solutions, MultiAR supports various AR devices, including h...

At the Worldwide Developers Conference in June 2023, Apple introduced the Vision Pro. The Apple Vision Pro (AVP) is a mixed reality headset; more specifically, it is a virtual reality device with an additional video see-through capability. The video see-through capability turns the AVP into an augmented reality (AR) device. The AR feature is enable...

Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computer vision , with applications in areas such as augmented reality (AR) and medical imaging. This work explores the intersection of two research trends: the integration of AR into image-guided surgery and the use of deep learning for point...

Diminished Reality is a technique for the removal of objects from the surroundings, providing a better view of otherwise obstructed features. This work explores the application of Diminished Reality in a medical setting, specifically aiming to visually eliminate surgical tools from operation sites for improved visibility and inspection. To this end...

(1) Background: This study aimed to integrate an augmented reality (AR) image-guided surgery (IGS) system, based on preoperative cone beam computed tomography (CBCT) scans, into clinical practice. (2) Methods: In preclinical and clinical surgical setups, an AR-guided visualization system based on Microsoft’s HoloLens 2 was assessed for complex lowe...

Diminished reality (DR) refers to the removal of real objects from the environment by virtually replacing them with their background. Modern DR frameworks use inpaint-ing to hallucinate unobserved regions. While recent deep learning-based inpainting is promising, the DR use case is complicated by the need to generate coherent structure and 3D geome...

Diminished reality (DR) involves virtually removing real objects from the environment using inpainting techniques. However, existing methods struggle with maintaining coherent structure and 3D geometry, particularly for advanced tasks like 3D scene editing. In response, we introduce DeepDR, a real-time RGB-D inpainting framework tailored for DR, en...

Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computer vision, with applications in areas such as augmented reality (AR) and medical imaging. This work explores the intersection of two research trends: the integration of AR into image-guided surgery and the use of deep learning for point c...

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...

In recent years, 3D printing (3DP) has gained importance in various fields. This technology has numerous applications, particularly in medicine. This contribution provides an overview on the state of the art of 3DP in medicine and showcases its current use in different medical disciplines and for medical education. In this meta-review, we provide a...

Since its release at the end of 2022, ChatGPT has seen a tremendous rise in attention, not only from the general public, but also from medical researchers and healthcare professionals. ChatGPT definitely changed the way we can communicate now with computers. We still remember the limitations of (voice) assistants, like Alexa or Siri, that were "ove...

In the dynamic landscape of digitized healthcare, open science principles are instrumental in driving transformative changes. This contribution describes two open science initiatives: StudierFenster, a cloud-based framework for (bio-)medical image analysis, and MedShapeNet, a comprehensive and open-access dataset of medical shapes. StudierFenster o...

We introduce two open science initiatives: StudierFenster, an open, browser-based framework for biomedical image analysis, and MedShapeNet, a comprehensive repository of medical shapes.

The concept of reality-virtuality (RV) continuum was introduced by Paul Milgram and Fumio Kishino in 1994. It describes a spectrum that ranges from a purely physical reality (the real world) to a purely virtual reality (a completely computer-generated environment), with various degrees of mixed reality in between. This continuum is "realized" by di...

Diminished reality (DR) refers to the removal of real objects from the environment by virtually replacing them with their background. Modern DR frameworks use inpaint-ing to hallucinate unobserved regions. While recent deep learning-based inpainting is promising, the DR use case is complicated by the need to generate coherent structure and 3D geome...

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 high-resolution shapes represented as binary voxel occupancy grids. In contrast to a dense CNN that takes the entire voxel grid...

The availability of computational hardware and developments in (medical) machine learning (MML) increases medical mixed realities' (MMR) clinical usability. Medical instruments have played a vital role in surgery for ages. To further accelerate the implementation of MML and MMR, three-dimensional (3D) datasets of instruments should be publicly avai...

Since its release at the end of 2022, the social response to ChatGPT, a large language model (LLM), has been huge, as it has revolutionized the way we communicate with computers. This review was performed to describe the technical background of LLMs and to provide a review of the current literature on LLMs in the field of oral and maxillofacial sur...

Implementation of augmented reality (AR) image guidance systems using preoperative cone beam computed tomography (CBCT) scans in apicoectomies promises to help surgeons overcome iatrogenic complications associated with this procedure. This study aims to evaluate the intraoperative feasibility and usability of HoloLens 2, an established AR image gui...

UNSTRUCTURED The concept of reality-virtuality (RV) continuum was introduced by Paul Milgram and Fumio Kishino in 1994. It describes a spectrum that ranges from a purely physical reality (the real world) to a purely virtual reality (a completely computer-generated environment), with various degrees of mixed reality in between. This continuum is “re...

UNSTRUCTURED At the Worldwide Developers Conference in June 2023, Apple introduced the Vision Pro. The Apple Vision Pro (AVP) is a mixed reality headset; more specifically, it is a virtual reality device with an additional video see-through capability. The video see-through capability turns the AVP into an augmented reality (AR) device. The AR feat...

We present MedShapeNet, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D surgical instrument models. Prior to the deep learning era, the broad application of statistical shape models (SSMs) in medical image analysis is evidence that shapes have been commonly used to describe medical data. Nowadays, however, state-of-the...

At the Worldwide Developers Conference (WWDC) in June 2023, Apple introduced the Vision Pro. The Vision Pro is a Mixed Reality (MR) headset, more specifically it is a Virtual Reality (VR) device with an additional Video See-Through (VST) capability. The VST capability turns the Vision Pro also into an Augmented Reality (AR) device. The AR feature i...

Surveillance imaging of patients with chronic aortic diseases, such as aneurysms and dissections, relies on obtaining and comparing cross-sectional diameter measurements along the aorta at predefined aortic landmarks, over time. The orientation of the cross-sectional measuring planes at each landmark is currently defined manually by highly trained...

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To a...

Over the last years, new medical imaging modalities were developed to help detect, diagnose and monitor different illnesses. Historically, the interpretation and analysis of the images obtained with these methods was conducted by trained radiologists or physicians, but, in recent years, with the development of more and more sophisticated computatio...

Nowadays, traditional interpretation of medical images by trained radiologists based on single image features is increasingly supported by radiomics approaches. This means that medical images are analyzed based on hundreds to thousands of radiomic features that are extracted in an automated fashion. Radiomics refers not only to the extraction of la...

The number of digital medical images is growing constantly over the years. This opens new possibilities of extracting information from them using computer-assisted methods, such as artificial intelligence. In this context, the application of radiomics has received increasing attention since 2012. In radiomics, medical image data is exploited by ext...

The aorta is the largest vessel of the human body and its pathological degenerations, such as dissections and aneurysms, can be life threatening. An automatic and fast segmentation of the aorta can therefore be a helpful tool to quickly identify an abnormal anatomy. The segmentation of the aortic vessel tree (AVT) typically requires extensive manua...

The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality (AR) display, is the main player in the recent boost in medical AR research. In this systematic review, we provide a comprehensive overview of the usage of the first-generation HoloLens within the medical domain, from its release in March 2016, until t...

Head and neck cancer has great regional anatomical complexity, as it can develop in different structures, exhibiting diverse tumour manifestations and high intratumoural heterogeneity, which is highly related to resistance to treatment, progression, the appearance of metastases, and tumour recurrences. Radiomics has the potential to address these o...

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...

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...

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...

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...

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...

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...

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...

This work analyses filters for vessel enhancement considering aortic dissection (AD).

This contribution presents a streamlined data pipeline to bring medical 3D scans onto Augmented Reality (AR) hardware.

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.

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...