Correlation of cone beam computed tomography (CBCT) findings in the maxillary sinus with dental diagnoses: a retrospective cross-sectional study.

This study was conducted to assess the coincidence of mucosal hyperplasia in the maxillary sinus and related clinical diagnoses of posterior maxillary teeth found in cone beam computed tomography (CBCT) scans. A total of 204 patients who underwent CBCT examinations between 2006 and 2008 were evaluated retrospectively. Clinical and CBCT findings were correlated using patient records. Absolute frequencies, odds ratios (OR), and 95% confidence intervals (95% CI) were calculated for statistical evaluations. There was a pronounced association between periodontitis and radiological signs of sinusitis. Basal mucosal wall thickening was more likely in patients with decayed and non-vital teeth compared to patients with sound teeth (OR = 5.2; 95% CI = 1.2-23.1). Basal mucosal wall thickening was also more likely than total mucosal thickening (OR = 10.4; 95% CI = 2.6-42.2). Patients with decayed and endodontically treated teeth were more likely to exhibit involvement of the basal wall (OR = 9.2; 95% CI = 3.3-25.2) than were patients with healthy teeth. CBCT examinations revealed a correlation between basal mucosal thickening in the maxillary sinus and decayed posterior maxillary teeth or periodontitis. Chronic symptoms involving the sinuses are one of the most common reasons for patients to consult physicians. One reason for chronic orofacial pain is the prevalence of undiagnosed sinus conditions.

Counting touching cell nuclei using fast ellipse detection to assess in vitro cell characteristics: a feasibility study.

In this article, we describe a new image analysis software that allows rapid segmentation and separation of fluorescently stained cell nuclei using a fast ellipse detection algorithm. Detection time ranged between 1.84 and 3.14 s. Segmentation results were compared with manual evaluation. The achieved over-segmentation rate was 0.11 (0.1 double counts and 0.01 false positive detections), and the under-segmentation rate was of 0.03 over all images. We demonstrate the applicability of the proposed algorithm to automated counting of fluorescent-labeled cell nuclei and to tissue characterization. Moreover, the performance of the proposed algorithm is compared with preexisting automated image analysis techniques described by others.

Implementation of new software for fast screening of cell compatibility on surface modifications using low-contrast time-lapsed microscopy.

Assessment of cell adhesion and cell size provides valuable information on surface biocompatibility. However, most investigations on cell morphology dynamics are time and resource consuming, of rather descriptive character and lack procedures for appropriate quantification. The aim of the study was to develop a software programme which allows automated cell segmentation and identification as well as calculation and further processing of cell size in low-contrast images. The software utilises modified edge detection and morphologic operations for automatic cell analysis in light microscopy images. In an application study, osteogenic cell-adhesion dynamics were quantified for the ECM proteins collagen type I (COL) and fibronectin (FIB) over a period of 12 hrs. Untreated tissue culture polystyrene (TCPS) served as control. The software programme proofed full function in automatic cell tracking and quantification of cell size. After 11 h, cell sizes were highest for COL (6391 ± 1167 µm(2)) and FIB (6036 ± 411 µm(2)) compared with TCPS (3261 ± 693 µm(2)). The developed software allows quantification of initial cell size changes on translucent surface modifications and is suitable as a reliable tool for fast biocompatibility screening. Osteogenic cell adhesion was significantly promoted by COL and FIB indicating the potential of respective functionalized biomaterial surfaces.

Advantages of fitting contrast curves using logistic function: a technical note.

OBJECTIVE: The aim of this article is to demonstrate how the contrast properties of an imaging system can be ideally fitted with the use of stripe patterns and the logistic function. STUDY DESIGN: Stripe patterns with defined amounts of line pairs (lp/mm) per mm (10-20 lp/mm) were recorded with the use of digital photostimulable storage phosphor. Scan data and normalized image data were analyzed with the use of ImageJ and MatLab to calculate different contrast curves. RESULTS: For original scan data, the goodness of fit was 0.0000019 (sum of squared error [SSE]). The R-square was 0.9998. For normalized data the goodness of fit was 0.0007 (SSE) and the R-square 0.998. An amount of 50% contrast could be calculated to be found on 11.67 lp/mm in normalized images. CONCLUSIONS: This article addresses a potentially new approach to compare digital x-ray modalities using a direct assessment of a known technical target.

Contrast curves of five different intraoral X-ray sensors: a technical note.

OBJECTIVES: The aim of this article is to compare the contrast and resolution properties of five different sensors using a known technical target. METHODS: Stripe patterns with defined amounts of line pairs per mm (2.5-20 LP/mm) were recorded using five commercial digital sensors. Image data were analyzed using ImageJ and MatLab to calculate different contrast curves using logistic regression. RESULTS: The Dexis Platinum Sensor reached a calculated 10% contrast at 29.52 LP/mm. The Duerr VistaRay 6 Sensor reached a 10% contrast at 9.9 LP/mm. The 10% contrast was found at 18.8 LP/mm for the Duerr VistaRay 7. The Sirona Xios+ Sensor reached a calculated 10% contrast at 13.9 LP/mm. The Sirona Fullsize charge-coupled device (CCD) Sensor exhibited 10% contrast at 10.3 LP/mm. CONCLUSIONS: The contrast transfer function assessment used in the study confirmed that the spatial frequency at 10% contrast was much lower than the theoretical resolution computed from the pixel size.

Intraoral cameras as a computer-aided diagnosis tool for root canal orifices.

The aim of this study was to evaluate the diagnostic advantage of a new software tool in combination with an intraoral camera for automatic detection of root canal orifices in life videos via the access cavity of extracted human molars. The performance of a predoctoral dental student analyzing the images of the camera (without automatic detection) was compared with that of an experienced observer. Sensitivity and confidence intervals were provided and compared with histological slices of 200 teeth used for evaluation. The software's sensitivity for detection of root canal orifices was 0.957 (95 percent confidence interval: 0.936 to 0.972). The sensitivity for the observer was 0.906 (95 percent confidence interval: 0.877 to 0.929) compared to 0.847 (95 percent confidence interval: 0.813 to 0.877) achieved by the predoctoral student. The tested software reaches a high sensitivity for automatic real-time detection of root canal orifices with intraoral cameras in direct comparison to histological images. The system might be a useful help for both pre-and postdoctoral students as an aid for the detection of second mesiobuccal root canal orifices.

Recognition of root canal orifices in video sequences as a future support system during endodontic treatment.

INTRODUCTION: The objective of this study was to show the practical application of computer-aided techniques for detecting root canal orifices through the access cavity using a video camera mounted on a microscope. METHODS: A minimum distance classification image recognition algorithm was tested in an in vitro study to assess the possibilities of computer-aided recognition of root canal orifices. A Motic DM143 digital stereo microscope (Motic Germany GmbH, Wetzlar, Germany) was used because it includes a video camera that can be connected via USB1.1 to any computer. RESULTS: The newly developed software is capable of communicating with a video camera and can automatically detect the root canal orifices in all teeth used in this study. A total of 165 extracted human teeth (molars and premolars) were used as test data to collect 8,250 images via screenshots for the evaluation of the detection quality. The software provided a detection sensitivity of 90.1%, with only 11.9% of the images as false-positive detections. CONCLUSION: The study shows that computer-aided recognition of root canal orifices with video cameras is possible.