Manual segmentation of computed tomography (CT) datasets was performed for robot-assisted endoscope movement during functional endoscopic sinus surgery (FESS). Segmented 3D models are needed for the robots' workspace definition. A total of 50 preselected CT datasets were each segmented in 150-200 coronal slices with 24 landmarks being set. Three different colors for segmentation represent diverse risk areas. Extension and volumetric measurements were performed. Three-dimensional reconstruction was generated after segmentation. Manual segmentation took 8-10 h for each CT dataset. The mean volumes were: right maxillary sinus 17.4 cm(3), left side 17.9 cm(3), right frontal sinus 4.2 cm(3), left side 4.0 cm(3), total frontal sinuses 7.9 cm(3), sphenoid sinus right side 5.3 cm(3), left side 5.5 cm(3), total sphenoid sinus volume 11.2 cm(3). Our manually segmented 3D-models present the patient's individual anatomy with a special focus on structures in danger according to the diverse colored risk areas. For safe robot assistance, the high-accuracy models represent an average of the population for anatomical variations, extension and volumetric measurements. They can be used as a database for automatic model-based segmentation. None of the segmentation methods so far described provide risk segmentation. The robot's maximum distance to the segmented border can be adjusted according to the differently colored areas.

The Australian GPs in this study identify a role for themselves in ADHD care which is largely supportive in nature, and involves close liaison with specialist services.

Segmentation of medical image data is getting more and more important over the last years. The results are used for diagnosis, surgical planning or workspace definition of robot-assisted systems. The purpose of this paper is to find out whether manual or semi-automatic segmentation is adequate for ENT surgical workflow or whether fully automatic segmentation of paranasal sinuses and nasal cavity is needed. We present a comparison of manual and semi-automatic segmentation of paranasal sinuses and the nasal cavity. Manual segmentation is performed by custom software whereas semi-automatic segmentation is realized by a commercial product (Amira). For this study we used a CT dataset of the paranasal sinuses which consists of 98 transversal slices, each 1.0 mm thick, with a resolution of 512 x 512 pixels. For the analysis of both segmentation procedures we used volume, extension (width, length and height), segmentation time and 3D-reconstruction. The segmentation time was reduced from 960 minutes with manual to 215 minutes with semi-automatic segmentation. We found highest variances segmenting nasal cavity. For the paranasal sinuses manual and semi-automatic volume differences are not significant. Dependent on the segmentation accuracy both approaches deliver useful results and could be used for e.g. robot-assisted systems. Nevertheless both procedures are not useful for everyday surgical workflow, because they take too much time. Fully automatic and reproducible segmentation algorithms are needed for segmentation of paranasal sinuses and nasal cavity.

Manual segmentation is often used for evaluation of automatic or semi-automatic segmentation. The purpose of this paper is to describe the inter and intraindividual variability, the dubiety of manual segmentation as a gold standard and to find reasons for the discrepancy. We realized two experiments. In the first one ten ENT surgeons, ten medical students and one engineer outlined the right maxillary sinus and ethmoid sinuses manually on a standard CT dataset of a human head. In the second experiment two participants outlined maxillary sinus and ethmoid sinuses five times consecutively. Manual segmentation was accomplished with custom software using a line segmentation tool. The first experiment shows the interindividual variability of manual segmentation which is higher for ethmoidal sinuses than for maxillary sinuses. The variability can be caused by the level of experience, different interpretation of the CT data or different levels of accuracy. The second experiment shows intraindividual variability which is lower than interindividual variability. Most variances in both experiments appear during segmentation of ethmoidal sinuses and outlining hiatus semilunaris. Concerning the inter and intraindividual variances the segmentation result of one manual segmenter could not directly be used as gold standard for the evaluation of automatic segmentation algorithms.

The objective of this study is to examine the factors that influence teachers’ acceptance of digital tools for undertaking distance teaching during the Covid-19 pandemic. Based on the variables of the technology acceptance model, we have conducted interviews with 15 secondary school teachers with varying degrees of professional experiences and combinations of subjects, from the federal state of Baden-Wuerttemberg in Germany and analyzed the same. The results indicate that, other than user motivation, three areas, namely “regulations and specifications,” “technological infrastructure,” and “heterogeneity of students and teachers,” affect the adoption of digital tools. The Covid-19 pandemic, which inevitably led teachers to embrace digital tools, positively influenced the perception and immediate usefulness of digital tools. We assert that no other variable would have been able to universally influence technology usage and acceptance to such an extent as to replicate the findings of our study and simultaneously highlight the uniqueness of the current situation and the necessity for examining its impact.