@inproceedings{SiqueiraRodriguesNyakaturaZachowetal.2022, author = {Siqueira Rodrigues, Lucas and Nyakatura, John and Zachow, Stefan and Israel, Johann Habakuk}, title = {An Immersive Virtual Paleontology Application}, 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}, year = {2022}, 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} } @phdthesis{Sahu2022, author = {Sahu, Manish}, title = {Vision-based Context-awareness in Minimally Invasive Surgical Video Streams}, pages = {103}, year = {2022}, abstract = {Surgical interventions are becoming increasingly complex thanks to modern assistance systems (imaging, robotics, etc.). Minimally invasive surgery in particular places high demands on surgeons due to added surgical complexity and information overload. Therefore, there is a growing need of developing context-aware systems that recognize the current surgical situation in order to derive and present the relevant information to the surgical staff for assistance. Current approaches for deriving contextual cues either utilize specialized hardware that is disruptive to the surgical workflow, or utilize vision-based approaches that require valuable time of surgeons, especially for manual annotations. The main objective of this cumulative dissertation is to improve the existing approaches for three important sub-problems of vision-based context-aware systems, namely surgical phase recognition, surgical instrument recognition and surgical instrument segmentation, while tackling the vision and manual annotation challenges related to these problems. This dissertation demonstrates that vision-based approaches for the three named clinical sub-problems of context-aware systems can be developed in an annotation-scarce setting by employing: domain-specific, deep learning based transfer learning techniques for the surgical instrument and phase recognition tasks; and deep learning based simulation-to-real unsupervised domain adaptation techniques for the surgical instrument segmentation task. The efficacy and real-time performance of the developed approaches have been evaluated on publicly available datasets containing real surgical videos (laparoscopic procedures) that were acquired in an uncontrolled surgical environment. These proposed approaches advance the state-of-the-art for the aforementioned research problems of context-aware systems in the OR and can potentially be utilized for real-time notification of the surgical phase, surgical instrument usage and image-based localization of surgical instruments.}, language = {en} } @misc{AmbellanZachowvonTycowicz2021, author = {Ambellan, Felix and Zachow, Stefan and von Tycowicz, Christoph}, title = {Geodesic B-Score for Improved Assessment of Knee Osteoarthritis}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-81930}, year = {2021}, abstract = {Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination ability over its Euclidean counterpart, which we demonstrate for predictive validity on assessing risks of total knee replacement. This result highlights the potential of the geodesic B-score to enable improved personalized assessment and stratification for interventions.}, language = {en} } @article{SunkaraHeinzHeinrichetal.2021, author = {Sunkara, Vikram and Heinz, Gitta A. and Heinrich, Frederik F. and Durek, Pawel and Mobasheri, Ali and Mashreghi, Mir-Farzin and Lang, Annemarie}, title = {Combining segmental bulk- and single-cell RNA-sequencing to define the chondrocyte gene expression signature in the murine knee joint}, volume = {29}, journal = {Osteoarthritis and Cartilage}, number = {6}, doi = {10.1016/j.joca.2021.03.007}, pages = {905 -- 914}, year = {2021}, language = {en} } @article{HerterHegeHadwigeretal.2021, author = {Herter, Felix and Hege, Hans-Christian and Hadwiger, Markus and Lepper, Verena and Baum, Daniel}, title = {Thin-Volume Visualization on Curved Domains}, volume = {40}, journal = {Computer Graphics Forum}, number = {3}, publisher = {Wiley-Blackwell Publishing Ltd.}, address = {United Kingdom}, doi = {10.1111/cgf.14296}, pages = {147 -- 157}, year = {2021}, abstract = {Thin, curved structures occur in many volumetric datasets. Their analysis using classical volume rendering is difficult because parts of such structures can bend away or hide behind occluding elements. This problem cannot be fully compensated by effective navigation alone, because structure-adapted navigation in the volume is cumbersome and only parts of the structure are visible in each view. We solve this problem by rendering a spatially transformed view into the volume so that an unobscured visualization of the entire curved structure is obtained. As a result, simple and intuitive navigation becomes possible. The domain of the spatial transform is defined by a triangle mesh that is topologically equivalent to an open disc and that approximates the structure of interest. The rendering is based on ray-casting in which the rays traverse the original curved sub-volume. In order to carve out volumes of varying thickness, the lengths of the rays as well as the position of the mesh vertices can be easily modified in a view-controlled manner by interactive painting. We describe a prototypical implementation and demonstrate the interactive visual inspection of complex structures from digital humanities, biology, medicine, and materials science. Displaying the structure as a whole enables simple inspection of interesting substructures in their original spatial context. Overall, we show that transformed views utilizing ray-casting-based volume rendering supported by guiding surface meshes and supplemented by local, interactive modifications of ray lengths and vertex positions, represent a simple but versatile approach to effectively visualize thin, curved structures in volumetric data.}, language = {en} } @article{BaumHerterLarsenetal.2021, author = {Baum, Daniel and Herter, Felix and Larsen, John M{\o}ller and Lichtenberger, Achim and Raja, Rubina}, title = {Revisiting the Jerash Silver Scroll: a new visual data analysis approach}, volume = {21}, journal = {Digital Applications in Archaeology and Cultural Heritage}, doi = {10.1016/j.daach.2021.e00186}, pages = {e00186}, year = {2021}, abstract = {This article revisits a complexly folded silver scroll excavated in Jerash, Jordan in 2014 that was digitally examined in 2015. In this article we apply, examine and discuss a new virtual unfolding technique that results in a clearer image of the scroll's 17 lines of writing. We also compare it to the earlier unfolding and discuss progress in general analytical tools. We publish the original and the new images as well as the unfolded volume data open access in order to make these available to researchers interested in optimising unfolding processes of various complexly folded materials.}, language = {en} } @article{LiangPiaoBeuscheletal.2021, author = {Liang, YongTian and Piao, Chengji and Beuschel, Christine B. and Toppe, David and Kollipara, Laxmikanth and Bogdanow, Boris and Maglione, Marta and L{\"u}tzkendorf, Janine and See, Jason Chun Kit and Huang, Sheng and Conrad, Tim and Kintscher, Ulrich and Madeo, Frank and Liu, Fan and Sickmann, Albert and Sigrist, Stephan J.}, title = {eIF5A hypusination, boosted by dietary spermidine, protects from premature brain aging and mitochondrial dysfunction}, volume = {35}, journal = {Cell Reports}, number = {2}, doi = {10.1016/j.celrep.2021.108941}, year = {2021}, language = {de} } @article{MelnykMontavonKlusetal.2020, author = {Melnyk, Kateryna and Montavon, Gr{\`e}goire and Klus, Stefan and Conrad, Tim}, title = {Graph Kernel Koopman Embedding for Human Microbiome Analysis}, volume = {5}, journal = {Applied Network Science}, number = {96}, doi = {10.1007/s41109-020-00339-2}, year = {2020}, abstract = {More and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, or some cancer types. Thanks to modern high-throughput omics technologies, it becomes possible to directly analyze human microbiome and its influence on the health status. Microbial communities are monitored over long periods of time and the associations between their members are explored. These relationships can be described by a time-evolving graph. In order to understand responses of the microbial community members to a distinct range of perturbations such as antibiotics exposure or diseases and general dynamical properties, the time-evolving graph of the human microbial communities has to be analyzed. This becomes especially challenging due to dozens of complex interactions among microbes and metastable dynamics. The key to solving this problem is the representation of the time-evolving graphs as fixed-length feature vectors preserving the original dynamics. We propose a method for learning the embedding of the time-evolving graph that is based on the spectral analysis of transfer operators and graph kernels. We demonstrate that our method can capture temporary changes in the time-evolving graph on both synthetic data and real-world data. Our experiments demonstrate the efficacy of the method. Furthermore, we show that our method can be applied to human microbiome data to study dynamic processes.}, language = {en} } @article{IravaniConrad2023, author = {Iravani, Sahar and Conrad, Tim}, title = {An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data}, volume = {20}, journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, number = {1}, doi = {10.1109/tcbb.2022.3141656}, pages = {151 -- 161}, year = {2023}, abstract = {Analyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise. Additionally, DL-based workflows are often hindered to be integrated into medical settings due to the lack of interpretable explanation. We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying complex protein mixtures. Our DLearnMS framework learns the clinical state of LC-MS data instances using convolutional neural networks. Based on the trained neural networks, we show how biomarkers can be identified using layer-wise relevance propagation. This enables detecting discriminating regions of the data and the design of more robust networks. One of the main advantages over other established methods is that no explicit preprocessing step is needed in our DLearnMS framework. Our evaluation shows that DLearnMS outperforms conventional LC-MS biomarker detection approaches in identifying fewer false positive peaks while maintaining a comparable amount of true positives peaks.}, language = {en} } @article{RamsConrad2022, author = {Rams, Mona and Conrad, Tim}, title = {Dictionary learning allows model-free pseudotime estimation of transcriptomics data}, volume = {23}, journal = {BMC Genomics}, publisher = {BioMed Central}, doi = {10.1186/s12864-021-08276-9}, year = {2022}, language = {en} }