Refine
Document Type
- Journal article (28)
- Conference proceeding (25)
- Book chapter (1)
Has full text
- yes (54) (remove)
Is part of the Bibliography
- yes (54)
Institute
- Informatik (54)
Publisher
Introduction: Even if there is a standard procedure of CI surgery, especially in pediatric surgery surgical steps often differ individually due to anatomical variations, malformations or unforseen events. This is why every surgical report should be created individually, which takes time and relies on the correct memory of the surgeon. A standardized recording of intraoperative data and subsequent storage as well as text processing would therefore be desirable and provides the basis for subsequent data processing, e.g. in the context of research or quality assurance.
Method: In cooperation with Reutlingen University, we conducted a workflow analysis of the prototype of a semi-automatic checklist tool. Based on automatically generated checklists generated from BPMN models a prototype user interface was developed for an android tablet. Functions such as uploading photos and files, manual user entries, the interception of foreseeable deviations from the normal course of operations and the automatic creation of OP documentation could be implemented. The system was tested in a remote usability test on a petrous bone model.
Result: The user interface allows a simple intuitive handling, which can be well implemented in the intraoperative setting. Clinical data as well as surgical steps could be individually recorded and saved via DICOM. An automatic surgery report could be created and saved.
Summary: The use of a dynamic checklist tool facilitates the capture, storage and processing of surgical data. Further applications in clinical practice are pending.
Ultra wideband real-time locating system for tracking people and devices in the operating room
(2022)
Position tracking within the OR could be one possible input for intraoperative situation recognition. Our approach demonstrates a Real-time Locating System (RTLS) using the Ultra Wideband (UWB) technology to determine the position of people or objects. The UWB RTLS was integrated into the research OR at Reutlingen University and the system’s settings were optimized regarding the four factors accuracy, susceptibility to interference, range, and latency. Therefore, different parameters were adapted and the effects on the factors were compared. Goodtracking quality could be achieved under optimal settings. These results indicate that a UWB RTLS is well suited to determine the position of people and devices in our setting. The feasibility of the system needsto be evaluated under real OR conditions.
With the progress of technology in modern hospitals, an intelligent perioperative situation recognition will gain more relevance due to its potential to substantially improve surgical workflows by providing situation knowledge in real-time. Such knowledge can be extracted from image data by machine learning techniques but poses a privacy threat to the staff’s and patients’ personal data. De-identification is a possible solution for removing visual sensitive information. In this work, we developed a YOLO v3 based prototype to detect sensitive areas in the image in real-time. These are then deidentified using common image obfuscation techniques. Our approach shows that it is principle suitable for de-identifying sensitive data in OR images and contributes to a privacyrespectful way of processing in the context of situation recognition in the OR.
Towards Automated Surgical Documentation using automatically generated checklists from BPMN models
(2021)
The documentation of surgeries is usually created from memory only after the operation, which is an additional effort for the surgeon and afflicted with the possibility of imprecisely, shortend reports. The display of process steps in the form of checklists and the automatic creation of surgical documentation from the completed process steps could serve as a reminder, standardize the surgical procedure and save time for the surgeon. Based on two works from Reutlingen University, which implemented the creation of dynamic checklists from Business Process Modelling Notation (BPMN) models and the storage of times at which a process step was completed, a prototype was developed for an android tablet, to expand the dynamic checklists by functions such as uploading photos and files, manual user entries, the interception of foreseeable deviations from the normal course of operations and the automatic creation of OR documentation.
Intraoperative brain deformation, so called brain shift, affects the applicability of preoperative magnetic resonance imaging (MRI) data to assist the procedures of intraoperative ultrasound (iUS) guidance during neurosurgery. This paper proposes a deep learning-based approach for fast and accurate deformable registration of preoperative MRI to iUS images to correct brain shift. Based on the architecture of 3D convolutional neural networks, the proposed deep MRI-iUS registration method has been successfully tested and evaluated on the retrospective evaluation of cerebral tumors (RESECT) dataset. This study showed that our proposed method outperforms other registration methods in previous studies with an average mean squared error (MSE) of 85. Moreover, this method can register three 3D MRI-US pair in less than a second, improving the expected outcomes of brain surgery.
Purpose
For the modeling, execution, and control of complex, non-standardized intraoperative processes, a modeling language is needed that reflects the variability of interventions. As the established Business Process Model and Notation (BPMN) reaches its limits in terms of flexibility, the Case Management Model and Notation (CMMN) was considered as it addresses weakly structured processes.
Methods
To analyze the suitability of the modeling languages, BPMN and CMMN models of a Robot-Assisted Minimally Invasive Esophagectomy and Cochlea Implantation were derived and integrated into a situation recognition workflow. Test cases were used to contrast the differences and compare the advantages and disadvantages of the models concerning modeling, execution, and control. Furthermore, the impact on transferability was investigated.
Results
Compared to BPMN, CMMN allows flexibility for modeling intraoperative processes while remaining understandable. Although more effort and process knowledge are needed for execution and control within a situation recognition system, CMMN enables better transferability of the models and therefore the system. Concluding, CMMN should be chosen as a supplement to BPMN for flexible process parts that can only be covered insufficiently by BPMN, or otherwise as a replacement for the entire process.
Conclusion
CMMN offers the flexibility for variable, weakly structured process parts, and is thus suitable for surgical interventions. A combination of both notations could allow optimal use of their advantages and support the transferability of the situation recognition system.
There are several intra-operative use cases which require the surgeon to interact with medical devices. We used the Leap Motion Controller as input device and implemented two use-cases: 2D-Interaction (e.g. advancing EPR data) and selection of a value (e.g. room illumination brightness). The gesture detection was successful and we mapped its output to several devices and systems.
Background
Personalized medicine requires the integration and analysis of vast amounts of patient data to realize individualized care. With Surgomics, we aim to facilitate personalized therapy recommendations in surgery by integration of intraoperative surgical data and their analysis with machine learning methods to leverage the potential of this data in analogy to Radiomics and Genomics.
Methods
We defined Surgomics as the entirety of surgomic features that are process characteristics of a surgical procedure automatically derived from multimodal intraoperative data to quantify processes in the operating room. In a multidisciplinary team we discussed potential data sources like endoscopic videos, vital sign monitoring, medical devices and instruments and respective surgomic features. Subsequently, an online questionnaire was sent to experts from surgery and (computer) science at multiple centers for rating the features’ clinical relevance and technical feasibility.
Results
In total, 52 surgomic features were identified and assigned to eight feature categories. Based on the expert survey (n = 66 participants) the feature category with the highest clinical relevance as rated by surgeons was “surgical skill and quality of performance” for morbidity and mortality (9.0 ± 1.3 on a numerical rating scale from 1 to 10) as well as for long-term (oncological) outcome (8.2 ± 1.8). The feature category with the highest feasibility to be automatically extracted as rated by (computer) scientists was “Instrument” (8.5 ± 1.7). Among the surgomic features ranked as most relevant in their respective category were “intraoperative adverse events”, “action performed with instruments”, “vital sign monitoring”, and “difficulty of surgery”.
Conclusion
Surgomics is a promising concept for the analysis of intraoperative data. Surgomics may be used together with preoperative features from clinical data and Radiomics to predict postoperative morbidity, mortality and long-term outcome, as well as to provide tailored feedback for surgeons.
Stent graft visualization and planning tool for endovascular surgery using finite element analysis
(2014)
Purpose: A new approach to optimize stent graft selection for endovascular aortic repair is the use of finite element analysis. Once the finite element model is created and solved, a software module is needed to view the simulation results in the clinical work environment. A new tool for Interpretation of simulation results, named Medical Postprocessor, that enables comparison of different stent graft configurations and products was designed, implemented and tested. Methods Aortic endovascular stent graft ring forces and sealing states in the vessel landing zone of three different configurations were provided in a surgical planning software using the Medical Imaging Interaction Tool Kit (MITK) Software system. For data interpretation, software modules for 2D and 3D presentations were implemented. Ten surgeons evaluated the software features of the Medical Postprocessor. These surgeons performed usability tests and answered questionnaires based on their experience with the system.
Results: The Medical Postprocessor visualization system enabled vascular surgeons to determine the configuration with the highest overall fixation force in 16 ± 6 s, best proximal sealing in 56±24 s and highest proximal fixation force in 38 ± 12 s. The majority considered the multiformat data provided helpful and found the Medical Postprocessor to be an efficient decision support system for stent graft selection. The evaluation of the user interface results in an ISONORMconform user interface (113.5 points).
Conclusion: The Medical Postprocessor visualization Software tool for analyzing stent graft properties was evaluated by vascular surgeons. The results show that the software can assist the interpretation of simulation results to optimize stent graft configuration and sizing.
One of the key challenges for automatic assistance is the support of actors in the operating room depending on the status of the procedure. Therefore, context information collected in the operating room is used to gain knowledge about the current situation. In literature, solutions already exist for specific use cases, but it is doubtful to what extent these approaches can be transferred to other conditions. We conducted a comprehensive literature research on existing situation recognition systems for the intraoperative area, covering 274 articles and 95 cross-references published between 2010 and 2019. We contrasted and compared 58 identified approaches based on defined aspects such as used sensor data or application area. In addition, we discussed applicability and transferability. Most of the papers focus on video data for recognizing situations within laparoscopic and cataract surgeries. Not all of the approaches can be used online for real-time recognition. Using different methods, good results with recognition accuracies above 90% could be achieved. Overall, transferability is less addressed. The applicability of approaches to other circumstances seems to be possible to a limited extent. Future research should place a stronger focus on adaptability. The literature review shows differences within existing approaches for situation recognition and outlines research trends. Applicability and transferability to other conditions are less addressed in current work.
Purpose
Computerized medical imaging processing assists neurosurgeons to localize tumours precisely. It plays a key role in recent image-guided neurosurgery. Hence, we developed a new open-source toolkit, namely Slicer-DeepSeg, for efficient and automatic brain tumour segmentation based on deep learning methodologies for aiding clinical brain research.
Methods
Our developed toolkit consists of three main components. First, Slicer-DeepSeg extends the 3D Slicer application and thus provides support for multiple data input/ output data formats and 3D visualization libraries. Second, Slicer core modules offer powerful image processing and analysis utilities. Third, the Slicer-DeepSeg extension provides a customized GUI for brain tumour segmentation using deep learning-based methods.
Results
The developed Slicer-DeepSeg was validated using a public dataset of high-grade glioma patients. The results showed that our proposed platform’s performance considerably outperforms other 3D Slicer cloud-based approaches.
Conclusions
Developed Slicer-DeepSeg allows the development of novel AI-assisted medical applications in neurosurgery. Moreover, it can enhance the outcomes of computer-aided diagnosis of brain tumours. Open-source Slicer-DeepSeg is available at github.com/razeineldin/Slicer-DeepSeg.
Purpose
Context awareness in the operating room (OR) is important to realize targeted assistance to support actors during surgery. A situation recognition system (SRS) is used to interpret intraoperative events and derive an intraoperative situation from these. To achieve a modular system architecture, it is desirable to de-couple the SRS from other system components. This leads to the need of an interface between such an SRS and context-aware systems (CAS). This work aims to provide an open standardized interface to enable loose coupling of the SRS with varying CAS to allow vendor-independent device orchestrations.
Methods
A requirements analysis investigated limiting factors that currently prevent the integration of CAS in today's ORs. These elicited requirements enabled the selection of a suitable base architecture. We examined how to specify this architecture with the constraints of an interoperability standard. The resulting middleware was integrated into a prototypic SRS and our system for intraoperative support, the OR-Pad, as exemplary CAS for evaluating whether our solution can enable context-aware assistance during simulated orthopedical interventions.
Results
The emerging Service-oriented Device Connectivity (SDC) standard series was selected to specify and implement a middleware for providing the interpreted contextual information while the SRS and CAS are loosely coupled. The results were verified within a proof of concept study using the OR-Pad demonstration scenario. The fulfillment of the CAS’ requirements to act context-aware, conformity to the SDC standard series, and the effort for integrating the middleware in individual systems were evaluated. The semantically unambiguous encoding of contextual information depends on the further standardization process of the SDC nomenclature. The discussion of the validity of these results proved the applicability and transferability of the middleware.
Conclusion
The specified and implemented SDC-based middleware shows the feasibility of loose coupling an SRS with unknown CAS to realize context-aware assistance in the OR.
Die Segmentierung und das Tracking von minimal-invasiven robotergeführten Instrumenten ist ein wesentlicher Bestandteil für verschiedene computer assistierte Eingriffe. Allerdings treten in der minimal-invasiven Chirurgie, die das Anwendungsfeld für den hier beschriebenen Ansatz darstellt, häufig Schwierigkeiten durch Reflexionen, Schatten oder visuelle Verdeckungen durch Rauch und Organe auf und erschweren die Segmentierung und das Tracking der Instrumente.
Dieser Beitrag stellt einen Deep Learning Ansatz für ein markerloses Tracking von minimal-invasiven Instrumenten vor und wird sowohl auf simulierten als auch realen Daten getestet. Es wird ein simulierter als auch realer Datensatz mit Ground Truth Kennzeichnung für die binäre Segmentierung von Instrument und Hintergrund erstellt. Für den simulierten Datensatz werden Bilder aus einem simulierten Instrument und realem Hintergrund zusammengesetzt. Im Falle des realen Datensatzes spricht man von der Zusammensetzung der Bilder aus einem realen Instrument und Hintergrund. Insgesamt wird auf den simulierten Daten eine Pixelgenauigkeit von 94.70 Prozent und auf den realen Daten eine Pixelgenauigkeit von 87.30 Prozent erreicht.
Motivation: Aim of this project is the automatic classification of total hip endoprosthesis (THEP) components in 2D Xray images. Revision surgeries of total hip arthroplasty (THA) are common procedures in orthopedics and trauma surgery. Currently, around 400.000 procedures per year are performed in the United States (US) alone. To achieve the best possible result, preoperative planning is crucial. Especially if parts of the current THEP system are to be retained.
Methods: First, a ground truth based on 76 X-ray images was created: We used an image processing pipeline consisting of a segmentation step performed by a convolutional neural network and a classification step performed by a support vector machine (SVM). In total, 11 classes (5 pans and 6 shafts) shall be classified.
Results: The ground truth generated was of good quality even though the initial segmentation was performed by technicians. The best segmentation results were achieved using a U-net architecture. For classification, SVM architectures performed much better than additional neural networks.
Conclusions: The overall image processing pipeline performed well, but the ground truth needs to be extended to include a broader variability of implant types and more examples per training class.
This study is about estimating the reproducibility of finding palpation points of three different anatomical landmarks in the human body (Xiphoid Process and the 2 Hip Crests) to support a navigated ultrasound application. On 6 test subjects with different body mass index the three palpation points were located five times by two examiners. The deviation from the target position was calculated and correlated to the fat thickness above each palpation point. The reproducibility of the measurements had a mean error of ≈13.5 mm +- 4 mm, which seems to be sufficient for the desired application field.
Die minimal-invasive Chirurgie (MIC) entwickelt sich durch den Einsatz von medizinischen Robotern wie dem da Vinci System von Intuitive Surgical stetig weiter. Hierdurch kann eine bessere oder gleichwertige Operation bei deutlich geringerer körperlicher Belastung des Operateurs erreicht werden. Dabei entstehen jedoch neue Problemstellungen wie beispielsweise Kollision zwischen Roboterarmen und die benötigte Zeit zum Einrichten einer geeigneten Roboterkonfiguration. Daher ist eine effiziente Vorbereitung und Planung der Interventionen erforderlich. Diese Arbeit präsentiert einen Ansatz für eine verbesserte Planung mit Augmented Reality (AR) und einer Robotik Simulationssoftware (RS). Die Robotik Simulation dient zur Berechnung einer Roboterkonfiguration unter Vorgabe der Port-Positionen. Augmented Reality wird verwendet, um die berechneten Pose in der realen Umgebung zu visualisieren und somit leichter in den Operationssaal zu übertragen.
Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, DeepSeg, nnU-Net, and DeepSCAN for automatic glioma boundaries detection in pre-operative MRI. It is worth noting that our ensemble models took first place in the final evaluation on the BraTS testing dataset with Dice scores of 0.9294, 0.8788, and 0.8803, and Hausdorf distance of 5.23, 13.54, and 12.05, for the whole tumor, tumor core, and enhancing tumor, respectively. Furthermore, the proposed ensemble method ranked first in the final ranking on another unseen test dataset, namely Sub-Saharan Africa dataset, achieving mean Dice scores of 0.9737, 0.9593, and 0.9022, and HD95 of 2.66, 1.72, 3.32 for the whole tumor, tumor core, and enhancing tumor, respectively.
Multi-dimensional patient data, such as time varying volume data, data of different imaging modalities, surface segmentations etc. are of growing importance in the clinical routine. For many use cases, it is of major importance to replicate a certain visualization of a data set created on one machine on a different computer using different software tools. Up until now, there exists no standardized methodology for this consistent presentation. We propose an extension of the Digital Imaging und Communications in Medicine (DICOM) called “Multi dimensional Presentation State” and outline scope and first results of the standardization process.
The paper describes how eye-tracking can be used to explore electronic patient records (EPR) in a sterile environment. As an information display, we used a system that we developed for the presentation of patient data and for supporting surgical hand disinfection. The eye-tracking was performed using the Tobii Eye Tracker 4C, and the connection between the eye-tracker and the HTML website was realized using the Tobii EyeX Chrome Extension. Interactions with the EPR are triggered by fixations of icons. The interaction was working as intended, but test persons reported a high mental load while using the system.