@article{RueckertRueckertPalm, author = {R{\"u}ckert, Tobias and R{\"u}ckert, Daniel and Palm, Christoph}, title = {Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art}, series = {Computers in Biology and Medicine}, volume = {169}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, address = {Amsterdam}, doi = {10.1016/j.compbiomed.2024.107929}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-69830}, pages = {24}, abstract = {In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify and characterize datasets used for method development and evaluation and quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images and videos. The paper focuses on methods that work purely visually, without markers of any kind attached to the instruments, considering both single-frame semantic and instance segmentation approaches, as well as those that incorporate temporal information. The publications analyzed were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were "instrument segmentation", "instrument tracking", "surgical tool segmentation", and "surgical tool tracking", resulting in a total of 741 articles published between 01/2015 and 07/2023, of which 123 were included using systematic selection criteria. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing the available potential for future developments.}, subject = {Deep Learning}, language = {en} } @article{PangerlSukulRuecketal., author = {Pangerl, Jonas and Sukul, Pritam and R{\"u}ck, Thomas and Fuchs, Patricia and Weigl, Stefan and Miekisch, Wolfram and Bierl, Rudolf and Matysik, Frank-Michael}, title = {An inexpensive UV-LED photoacoustic based real-time sensor-system detecting exhaled trace-acetone}, series = {Photoacoustics}, volume = {38}, journal = {Photoacoustics}, publisher = {Elsevier}, issn = {2213-5979}, doi = {10.1016/j.pacs.2024.100604}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-71279}, pages = {12}, abstract = {n this research we present a low-cost system for breath acetone analysis based on UV-LED photoacoustic spectroscopy. We considered the end-tidal phase of exhalation, which represents the systemic concentrations of volatile organic compounds (VOCs) - providing clinically relevant information about the human health. This is achieved via the development of a CO2-triggered breath sampling system, which collected alveolar breath over several minutes in sterile and inert containers. A real-time mass spectrometer is coupled to serve as a reference device for calibration measurements and subsequent breath analysis. The new sensor system provided a 3σ detection limit of 8.3 ppbV and an NNEA of 1.4E-9 Wcm 1Hz 0.5. In terms of the performed breath analysis measurements, 12 out of 13 fell within the error margin of the photoacoustic measurement system, demonstrating the reliability of the measurements in the field.}, language = {en} } @article{MaierSchlattlGuessetal., author = {Maier, Robert and Schlattl, Andreas and Guess, Thomas and Mottok, J{\"u}rgen}, title = {CausalOps - Towards an industrial lifecycle for causal probabilistic graphical models}, series = {Information and Software Technology}, journal = {Information and Software Technology}, publisher = {Elsevier}, issn = {0950-5849}, doi = {10.1016/j.infsof.2024.107520}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-73350}, abstract = {Context: Causal probabilistic graph-based models have gained widespread utility, enabling the modeling of cause-and-effect relationships across diverse domains. With their rising adoption in new areas, such as safety analysis of complex systems, software engineering, and machine learning, the need for an integrated lifecycle framework akin to DevOps and MLOps has emerged. Currently, such a reference for organizations interested in employing causal engineering is missing. This lack of guidance hinders the incorporation and maturation of causal methods in the context of real-life applications. Objective: This work contextualizes causal model usage across different stages and stakeholders and outlines a holistic view of creating and maintaining them within the process landscape of an organization. Method: A novel lifecycle framework for causal model development and application called CausalOps is proposed. By defining key entities, dependencies, and intermediate artifacts generated during causal engineering, a consistent vocabulary and workflow model to guide organizations in adopting causal methods are established. Results: Based on the early adoption of the discussed methodology to a real-life problem within the automotive domain, an experience report underlining the practicability and challenges of the proposed approach is discussed. Conclusion: It is concluded that besides current technical advancements in various aspects of causal engineering, an overarching lifecycle framework that integrates these methods into organizational practices is missing. Although diverse skills from adjacent disciplines are widely available, guidance on how to transfer these assets into causality-driven practices still need to be addressed in the published literature. CausalOps' aim is to set a baseline for the adoption of causal methods in practical applications within interested organizations and the causality community.}, language = {en} }