@article{AmiranashviliLuedkeLietal., author = {Amiranashvili, Tamaz and L{\"u}dke, David and Li, Hongwei Bran and Zachow, Stefan and Menze, Bjoern}, title = {Learning continuous shape priors from sparse data with neural implicit functions}, series = {Medical Image Analysis}, volume = {94}, journal = {Medical Image Analysis}, doi = {10.1016/j.media.2024.103099}, pages = {103099}, abstract = {Statistical shape models are an essential tool for various tasks in medical image analysis, including shape generation, reconstruction and classification. Shape models are learned from a population of example shapes, which are typically obtained through segmentation of volumetric medical images. In clinical practice, highly anisotropic volumetric scans with large slice distances are prevalent, e.g., to reduce radiation exposure in CT or image acquisition time in MR imaging. For existing shape modeling approaches, the resolution of the emerging model is limited to the resolution of the training shapes. Therefore, any missing information between slices prohibits existing methods from learning a high-resolution shape prior. We propose a novel shape modeling approach that can be trained on sparse, binary segmentation masks with large slice distances. This is achieved through employing continuous shape representations based on neural implicit functions. After training, our model can reconstruct shapes from various sparse inputs at high target resolutions beyond the resolution of individual training examples. We successfully reconstruct high-resolution shapes from as few as three orthogonal slices. Furthermore, our shape model allows us to embed various sparse segmentation masks into a common, low-dimensional latent space ā€” independent of the acquisition direction, resolution, spacing, and field of view. We show that the emerging latent representation discriminates between healthy and pathological shapes, even when provided with sparse segmentation masks. Lastly, we qualitatively demonstrate that the emerging latent space is smooth and captures characteristic modes of shape variation. We evaluate our shape model on two anatomical structures: the lumbar vertebra and the distal femur, both from publicly available datasets.}, language = {en} } @inproceedings{SiqueiraRodriguesRiehmZachowetal., author = {Siqueira Rodrigues, Lucas and Riehm, Felix and Zachow, Stefan and Israel, Johann Habakuk}, title = {VoxSculpt: An Open-Source Voxel Library for Tomographic Volume Sculpting in Virtual Reality}, series = {2023 9th International Conference on Virtual Reality (ICVR), Xianyang, China, 2023}, booktitle = {2023 9th International Conference on Virtual Reality (ICVR), Xianyang, China, 2023}, doi = {10.1109/ICVR57957.2023.10169420}, pages = {515 -- 523}, abstract = {Manual processing of tomographic data volumes, such as interactive image segmentation in medicine or paleontology, is considered a time-consuming and cumbersome endeavor. Immersive volume sculpting stands as a potential solution to improve its efficiency and intuitiveness. However, current open-source software solutions do not yield the required performance and functionalities. We address this issue by contributing a novel open-source game engine voxel library that supports real-time immersive volume sculpting. Our design leverages GPU instancing, parallel computing, and a chunk-based data structure to optimize collision detection and rendering. We have implemented features that enable fast voxel interaction and improve precision. Our benchmark evaluation indicates that our implementation offers a significant improvement over the state-of-the-art and can render and modify millions of visible voxels while maintaining stable performance for real-time interaction in virtual reality.}, language = {en} } @article{WagendorfNahlesVachetal., author = {Wagendorf, Oliver and Nahles, Susanne and Vach, Kirstin and Kernen, Florian and Zachow, Stefan and Heiland, Max and Fl{\"u}gge, Tabea}, title = {The impact of teeth and dental restorations on gray value distribution in cone-beam computer tomography - a pilot study}, series = {International Journal of Implant Dentistry}, volume = {9}, journal = {International Journal of Implant Dentistry}, number = {27}, doi = {10.1186/s40729-023-00493-z}, abstract = {Purpose: To investigate the influence of teeth and dental restorations on the facial skeleton's gray value distributions in cone-beam computed tomography (CBCT). Methods: Gray value selection for the upper and lower jaw segmentation was performed in 40 patients. In total, CBCT data of 20 maxillae and 20 mandibles, ten partial edentulous and ten fully edentulous in each jaw, respectively, were evaluated using two different gray value selection procedures: manual lower threshold selection and automated lower threshold selection. Two sample t tests, linear regression models, linear mixed models, and Pearson's correlation coefficients were computed to evaluate the influence of teeth, dental restorations, and threshold selection procedures on gray value distributions. Results: Manual threshold selection resulted in significantly different gray values in the fully and partially edentulous mandible. (pā€‰=ā€‰0.015, difference 123). In automated threshold selection, only tendencies to different gray values in fully edentulous compared to partially edentulous jaws were observed (difference: 58-75). Significantly different gray values were evaluated for threshold selection approaches, independent of the dental situation of the analyzed jaw. No significant correlation between the number of teeth and gray values was assessed, but a trend towards higher gray values in patients with more teeth was noted. Conclusions: Standard gray values derived from CT imaging do not apply for threshold-based bone segmentation in CBCT. Teeth influence gray values and segmentation results. Inaccurate bone segmentation may result in ill-fitting surgical guides produced on CBCT data and misinterpreting bone density, which is crucial for selecting surgical protocols.}, language = {en} } @inproceedings{SiqueiraRodriguesNyakaturaZachowetal., author = {Siqueira Rodrigues, Lucas and Nyakatura, John and Zachow, Stefan and Israel, Johann Habakuk}, title = {Design Challenges and Opportunities of Fossil Preparation Tools and Methods}, series = {Proceedings of the 20th International Conference on Culture and Computer Science: Code and Materiality}, booktitle = {Proceedings of the 20th International Conference on Culture and Computer Science: Code and Materiality}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, doi = {10.1145/3623462.3623470}, abstract = {Fossil preparation is the activity of processing paleontological specimens for research and exhibition purposes. In addition to traditional mechanical extraction of fossils, preparation presently comprises non-destructive digital methods that are part of a relatively new field, namely virtual paleontology. Despite significant technological advances, both traditional and digital preparation remain cumbersome and time-consuming endeavors. However, this field has received scarce attention from a human-computer interaction perspective. The present study aims to elucidate the state-of-the-art for paleontological fossil preparation in order to determine its main challenges and start a conversation regarding opportunities for creating novel designs that tackle the field's current issues. We conducted a qualitative study involving both technical preparators and virtual paleontologists. The study was divided into two parts: First, we assembled technical preparators and paleontology researchers in a focus group session to discuss their workflows, obtain a preliminary understanding of their issues, and ideate solutions based on their counterparts' workflows. Next, we conducted a series of contextual inquiries involving direct observation and semi-structured in-depth interviews. We transcribed our recordings and examined the data through theoretical and inductive thematic analysis, clustering emerging themes and applying concepts from human-computer interaction and related fields. Our findings report on challenges faced by traditional and digital fossil preparators and potential opportunities to improve their tools and workflows. We contribute with a novel analysis of fossil preparation from an HCI perspective.}, language = {en} } @inproceedings{LuedkeAmiranashviliAmbellanetal., author = {L{\"u}dke, David and Amiranashvili, Tamaz and Ambellan, Felix and Ezhov, Ivan and Menze, Bjoern and Zachow, Stefan}, title = {Landmark-free Statistical Shape Modeling via Neural Flow Deformations}, series = {Medical Image Computing and Computer Assisted Intervention - MICCAI 2022}, volume = {13432}, booktitle = {Medical Image Computing and Computer Assisted Intervention - MICCAI 2022}, publisher = {Springer, Cham}, doi = {10.1007/978-3-031-16434-7_44}, abstract = {Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shape reconstruction and image segmentation, but also shape generation and classification. Existing shape priors either require dense correspondence between training examples or lack robustness and topological guarantees. We present FlowSSM, a novel shape modeling approach that learns shape variability without requiring dense correspondence between training instances. It relies on a hierarchy of continuous deformation flows, which are parametrized by a neural network. Our model outperforms state-of-the-art methods in providing an expressive and robust shape prior for distal femur and liver. We show that the emerging latent representation is discriminative by separating healthy from pathological shapes. Ultimately, we demonstrate its effectiveness on two shape reconstruction tasks from partial data. Our source code is publicly available (https://github.com/davecasp/flowssm).}, language = {en} } @inproceedings{SiqueiraRodriguesNyakaturaZachowetal., author = {Siqueira Rodrigues, Lucas and Nyakatura, John and Zachow, Stefan and Israel, Johann Habakuk}, title = {An Immersive Virtual Paleontology Application}, series = {13th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2022}, booktitle = {13th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2022}, doi = {10.1007/978-3-031-06249-0}, pages = {478 -- 481}, abstract = {Virtual paleontology studies digital fossils through data analysis and visualization systems. The discipline is growing in relevance for the evident advantages of non-destructive imaging techniques over traditional paleontological methods, and it has made significant advancements during the last few decades. However, virtual paleontology still faces a number of technological challenges, amongst which are interaction shortcomings of image segmentation applications. Whereas automated segmentation methods are seldom applicable to fossil datasets, manual exploration of these specimens is extremely time-consuming as it impractically delves into three-dimensional data through two-dimensional visualization and interaction means. This paper presents an application that employs virtual reality and haptics to virtual paleontology in order to evolve its interaction paradigms and address some of its limitations. We provide a brief overview of the challenges faced by virtual paleontology practitioners, a description of our immersive virtual paleontology prototype, and the results of a heuristic evaluation of our design.}, language = {en} } @article{GlatzederKomnikAmbellanetal., author = {Glatzeder, Korbinian and Komnik, Igor and Ambellan, Felix and Zachow, Stefan and Potthast, Wolfgang}, title = {Dynamic pressure analysis of novel interpositional knee spacer implants in 3D-printed human knee models}, series = {Scientific Reports}, volume = {12}, journal = {Scientific Reports}, doi = {10.1038/s41598-022-20463-6}, abstract = {Alternative treatment methods for knee osteoarthritis (OA) are in demand, to delay the young (<ā€‰50 Years) patient's need for osteotomy or knee replacement. Novel interpositional knee spacers shape based on statistical shape model (SSM) approach and made of polyurethane (PU) were developed to present a minimally invasive method to treat medial OA in the knee. The implant should be supposed to reduce peak strains and pain, restore the stability of the knee, correct the malalignment of a varus knee and improve joint function and gait. Firstly, the spacers were tested in artificial knee models. It is assumed that by application of a spacer, a significant reduction in stress values and a significant increase in the contact area in the medial compartment of the knee will be registered. Biomechanical analysis of the effect of novel interpositional knee spacer implants on pressure distribution in 3D-printed knee model replicas: the primary purpose was the medial joint contact stress-related biomechanics. A secondary purpose was a better understanding of medial/lateral redistribution of joint loading. Six 3D printed knee models were reproduced from cadaveric leg computed tomography. Each of four spacer implants was tested in each knee geometry under realistic arthrokinematic dynamic loading conditions, to examine the pressure distribution in the knee joint. All spacers showed reduced mean stress values by 84-88\% and peak stress values by 524-704\% in the medial knee joint compartment compared to the non-spacer test condition. The contact area was enlarged by 462-627\% as a result of the inserted spacers. Concerning the appreciable contact stress reduction and enlargement of the contact area in the medial knee joint compartment, the premises are in place for testing the implants directly on human knee cadavers to gain further insights into a possible tool for treating medial knee osteoarthritis.}, language = {en} } @inproceedings{AmiranashviliLuedkeLietal., author = {Amiranashvili, Tamaz and L{\"u}dke, David and Li, Hongwei and Menze, Bjoern and Zachow, Stefan}, title = {Learning Shape Reconstruction from Sparse Measurements with Neural Implicit Functions}, series = {Medical Imaging with Deep Learning}, booktitle = {Medical Imaging with Deep Learning}, abstract = {Reconstructing anatomical shapes from sparse or partial measurements relies on prior knowledge of shape variations that occur within a given population. Such shape priors are learned from example shapes, obtained by segmenting volumetric medical images. For existing models, the resolution of a learned shape prior is limited to the resolution of the training data. However, in clinical practice, volumetric images are often acquired with highly anisotropic voxel sizes, e.g. to reduce image acquisition time in MRI or radiation exposure in CT imaging. The missing shape information between the slices prohibits existing methods to learn a high-resolution shape prior. We introduce a method for high-resolution shape reconstruction from sparse measurements without relying on high-resolution ground truth for training. Our method is based on neural implicit shape representations and learns a continuous shape prior only from highly anisotropic segmentations. Furthermore, it is able to learn from shapes with a varying field of view and can reconstruct from various sparse input configurations. We demonstrate its effectiveness on two anatomical structures: vertebra and femur, and successfully reconstruct high-resolution shapes from sparse segmentations, using as few as three orthogonal slices.}, language = {en} } @article{GreweLiuHildebrandtetal., author = {Grewe, Carl Martin and Liu, Tuo and Hildebrandt, Andrea and Zachow, Stefan}, title = {The Open Virtual Mirror Framework for Enfacement Illusions - Enhancing the Sense of Agency With Avatars That Imitate Facial Expressions}, series = {Behavior Research Methods}, journal = {Behavior Research Methods}, publisher = {Springer}, doi = {10.3758/s13428-021-01761-9}, language = {de} } @article{AmbellanZachowvonTycowicz, author = {Ambellan, Felix and Zachow, Stefan and von Tycowicz, Christoph}, title = {Rigid Motion Invariant Statistical Shape Modeling based on Discrete Fundamental Forms}, series = {Medical Image Analysis}, volume = {73}, journal = {Medical Image Analysis}, doi = {10.1016/j.media.2021.102178}, abstract = {We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. Additionally, as planar configurations form a submanifold in shape space, our representation allows for effective estimation of quasi-isometric surfaces flattenings. We evaluate the performance of our model w.r.t. shape-based classification of hippocampus and femur malformations due to Alzheimer's disease and osteoarthritis, respectively. In particular, we achieve state-of-the-art accuracies outperforming the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing biological shape variability, we carry out an analysis of specificity and generalization ability.}, language = {en} }