Refine
Document Type
- Journal article (27) (remove)
Language
- English (27) (remove)
Is part of the Bibliography
- yes (27)
Institute
- Informatik (27)
Publisher
- Springer (12)
- De Gruyter (10)
- Elsevier (1)
- IEEE (1)
- Taylor & Francis (1)
- de Gruyter (1)
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.
Intra-operative fluoroscopy-guided assistance system for transcatheter aortic valve implantation
(2014)
A new surgical assistance system has been developed to assist the correct positioning of the AVP during transapical TAVI. The developed assistance system automatically defines the target area for implanting the AVP under live 2-D fluoroscopy guidance. Moreover, this surgical assistance system works with low levels of contrast agent for the final deployment of AVP, reducing therefore long-term negative effects, such as renal failure in the elderly and high-risk patients.
Checklists are a valuable tool to ensure process quality and quality of care. To ensure proper integration in clinical processes, it would be desirable to generate checklists directly from formal process descriptions. Those checklists could also be used for user interaction in context-aware surgical assist systems. We built a tool to automatically convert Business Process Model and Notation (BPMN) process models to checklists displayed as HTML websites. Gateways representing decisions are mapped to checklist items that trigger dynamic content loading based on the placed checkmark. The usability of the resulting system was positively evaluated regarding comprehensibility and end-user friendliness.
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.
Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train The CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labelled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.
The focus of the developed maturity model was set on processes. The concept of the widespread CMM and its practices has been transferred to the perioperative domain and the concept of the new maturity model. Additional optimization goals and technological as well as networking-specific aspects enable a process- and object-focused view of the maturity model in order to ensure broad coverage of different subareas. The evaluation showed that the model is applicable to the perioperative field. Adjustments and extensions of the maturity model are future steps to improve the rating and classification of the new maturity model.
Purpose: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid attenuated inversion recovery (FLAIR) MRI modality can provide the physician with information about tumor infiltration. Therefore, this paper proposes a new generic deep learning architecture, namely DeepSeg, for fully automated detection and segmentation of the brain lesion using FLAIR MRI data.
Methods: The developed DeepSeg is a modular decoupling framework. It consists of two connected core parts based on an encoding and decoding relationship. The encoder part is a convolutional neural network (CNN) responsible for spatial information extraction. The resulting semantic map is inserted into the decoder part to get the full-resolution probability map. Based on modified U-Net architecture, different CNN models such as residual neural network (ResNet), dense convolutional network (DenseNet), and NASNet have been utilized in this study.
Results: The proposed deep learning architectures have been successfully tested and evaluated on-line based on MRI datasets of brain tumor segmentation (BraTS 2019) challenge, including s336 cases as training data and 125 cases for validation data. The dice and Hausdorff distance scores of obtained segmentation results are about 0.81 to 0.84 and 9.8 to 19.7 correspondingly.
Conclusion: This study showed successful feasibility and comparative performance of applying different deep learning models in a new DeepSeg framework for automated brain tumor segmentation in FLAIR MR images. The proposed DeepSeg is open source and freely available at https://github.com/razeineldin/DeepSeg/.
Access to clinical information during interventions is an important aspect to support the surgeon and his team in the OR. The OR-Pad research project aims at displaying clinically relevant information close to the patient during surgery. With the OR-Pad system, the surgeon shall be able to access case-specific information, displayed on a sterile-packaged, portable display device. Therefore, information shall be prepared before surgery and also be available afterwards. The project follows an user-centered design process. Within the third iteration, the interaction concept was finalized, resulting in an application that can be used in two modes, mobile and intraoperative, to support the surgeon before/after and during surgery, respectively. By supporting the surgeon perioperatively, it is expected to improve the information situation in the OR and thereby the quality of surgical results. Based on this concept, the system architecture was designed in detail, using a client-server architecture. Components, communication interfaces, exchanged data, and intended standards for data exchange of the OR-Pad system including connecting systems were conceived. Expert interviews by using a clickable prototype were conducted to evaluate the concepts.
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.
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.
Background and purpose: Transapical aortic valve replacement (TAVR) is a recent minimally invasive surgical treatment technique for elderly and high-risk patients with severe aortic stenosis. In this paper,a simple and accurate image-based method is introduced to aid the intra-operative guidance of TAVR procedure under 2-D X-ray fluoroscopy.
Methods: The proposed method fuses a 3-D aortic mesh model and anatomical valve landmarks with live 2-D fluoroscopic images. The 3-D aortic mesh model and landmarks are reconstructed from interventional X-ray C-arm CT system, and a target area for valve implantation is automatically estimated using these aortic mesh models.Based on template-based tracking approach, the overlay of visualized 3-D aortic mesh model, land-marks and target area of implantation is updated onto fluoroscopic images by approximating the aortic root motion from a pigtail catheter motion without contrast agent. Also, a rigid intensity-based registration algorithm is used to track continuously the aortic root motion in the presence of contrast agent.Furthermore, a sensorless tracking of the aortic valve prosthesis is provided to guide the physician to perform the appropriate placement of prosthesis into the estimated target area of implantation.
Results: Retrospective experiments were carried out on fifteen patient datasets from the clinical routine of the TAVR. The maximum displacement errors were less than 2.0 mm for both the dynamic overlay of aortic mesh models and image-based tracking of the prosthesis, and within the clinically accepted ranges. Moreover, high success rates of the proposed method were obtained above 91.0% for all tested patient datasets.
Conclusion: The results showed that the proposed method for computer-aided TAVR is potentially a helpful tool for physicians by automatically defining the accurate placement position of the prosthesis during the surgical procedure.
Purpose: Medical processes can be modeled using different methods and notations.Currently used modeling systems like Business Process Model and Notation (BPMN) are not capable of describing the highly flexible and variable medical processes in sufficient detail.
Methods: We combined two modeling systems, Business Process Management (BPM) and Adaptive Case Management (ACM), to be able to model non-deterministic medical processes. We used the new Standards Case Management Model and Notation (CMMN) and Decision Management Notation (DMN).
Results: First, we explain how CMMN, DMN and BPMN could be used to model non-deterministic medical processes. We applied this methodology to model 79 cataract operations provided by University Hospital Leipzig, Germany, and four cataract operations provided by University Eye Hospital Tuebingen, Germany. Our model consists of 85 tasks and about 20 decisions in BPMN. We were able to expand the system with more complex situations that might appear during an intervention.
Conclusion: An effective modeling of the cataract intervention is possible using the combination of BPM and ACM. The combination gives the possibility to depict complex processes with complex decisions. This combination allows a significant advantage for modeling perioperative processes.
In this paper a method for the generation of gSPM with ontology-based generalization was presented. The resulting gSPM was modeled with BPMN/BPMNsix in an efficient way and could be executed with BPMN workflow engines. In the next step the implementation of resource concepts, anatomical structures, and transition probabilities for workflow execution will be realized.
Purpose
Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice.
Methods
In this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent.
Results
NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN.
Conclusion
Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI.
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.
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.
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.
Intraoperative imaging can assist neurosurgeons to define brain tumours and other surrounding brain structures. Interventional ultrasound (iUS) is a convenient modality with fast scan times. However, iUS data may suffer from noise and artefacts which limit their interpretation during brain surgery. In this work, we use two deep learning networks, namely UNet and TransUNet, to make automatic and accurate segmentation of the brain tumour in iUS data. Experiments were conducted on a dataset of 27 iUS volumes. The outcomes show that using a transformer with UNet is advantageous providing an efficient segmentation modelling long-range dependencies between each iUS image. In particular, the enhanced TransUNet was able to predict cavity segmentation in iUS data with an inference rate of more than 125 FPS. These promising results suggest that deep learning networks can be successfully deployed to assist neurosurgeons in the operating room.