The study shows the validity of video as well as live observations for modeling Surgical Process Models. For routine use, the authors recommend live observations due to their flexibility and effectiveness. If high precision is needed or the SPM parameters are altered during the study, video observations are the preferable approach.

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.

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The computation of gSPMs is a new approach in medical engineering and medical informatics. It supports increased evidence, e.g. for the application of alternative surgical strategies, investments for surgical technology, optimization protocols, or surgical education. Furthermore, this may be applicable in more technical research fields, as well, such as the development of surgical workflow management systems for the operating room of the future.

The aim of this study is the evaluation of a navigation system (NaviBase) for ENT surgery. For this purpose, a new methodology for the evaluation of surgical and ergonomic system properties has been developed. The practicability of the evaluation instruments will be examined using the example of the overall assessment of the system in comparison with the current surgical standard and with other systems using clinical efficiency criteria. The evaluation is based on 102 ENT surgical applications; of these, 89 were functional endoscopic sinus surgeries (FESS). The evaluation of surgical and ergonomic performance factors was performed by seven ENT surgeons. To evaluate surgical system properties, the Level of Quality (LOQ) in 89 cases of the FESS was determined. It compares the existing information of the surgeon with that of the navigation system on a scale of 0 to 100 and with a mean value of 50 and places it in a relationship to the clinical impact. The intraoperative change of the planned surgical strategy (Change of Surgical Strategy) was documented. The ergonomic factors of the system with the categories of Overall Confidence (Trust), awareness of the situation (Situation Awareness), influence on the operating team, requirements for specific skills (Skill Set Requirement), and cognitive load (Workload Shift) were recorded for all surgical procedures as Level of Reliance (LOR). In the evaluation of the surgical system properties, an average evaluation of the quality of the information, as an LOQ of 63.59, resulted. Every second application of the navigation system (47.9%), on average, led to a change in the surgical strategy. An extension/enhancement of the indication of the endonasal approach through the use of the navigation system was shown in 7 of 102 (6.8%) cases. The completion of the resection in the FESS was rated by 74% of group I and 11% of group II as better in comparison with the standard approach. Total confidence shows a positive evaluation of 3.35 in the LOR. To supplement the evaluation of the navigation system, the technical parameters were included. The maximum deviation, Amax, of the displayed position of the reference value amounted to 1.93 mm. The average deviation was at 1.29 mm with an SD above all values, sd, of 0.29. The subsequent economic evaluation resulted in an effective average extra expenditure of time of 1.35 minutes per case. The overall evaluation of the system imparts application-relevant information beyond the technical details and permits comparability between different assistance systems.