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Background Despite the increasing use of digital impressions in orthodontics, this technique does not usually form part of the learning objectives in dental training. The aim of this study was to determine how students assess the user-friendliness of intraoral scanners compared to a conventional impression technique after a theoretical and practical teaching module. Methods Thirty-one dental students in their seventh semester (4th year) received and conducted digital (3 M, St. Paul, NM) and conventional (alginate) impressions from: (i) the dentist’s perspective, and (ii) the patient’s perspective. Each student completed four questionnaires to evaluate: (i) the user-friendliness of intraoral scanning, and (ii) intraoral scanning compared to the conventional method. Results Thirty (97%) students had not previously performed digital impressions. Twenty-four (77%) students were overall “very” or “rather” satisfied with the handling of the intraoral scanning method, and 18 (58%) preferred digital to alginate impressions from the dentist’s perspective. From the “patient’s” perspective, the students did not report any significant differences between the two methods. However, the impression tray in conventional impressions reduced “patient” comfort significantly more than the camera in digital impressions (Z = − 3.496, p  < 0.001). Conclusions Dental students were able to practice both conventional alginate and modern digital impressions without prior knowledge of intraoral impression techniques after basic training and an introduction from dentists. Students reported a preference for the digital technique. Implementing digital intraoral impressions into undergraduate training is recommended to familiarise students with this rapidly developing digital technique at an early stage.

Changes in ventricular rotation measured by two-dimensional speckle tracking echocardiography (2DSTE) are early indicators of cardiac disease. Data on the clinical feasibility of this important measure are scarce and there is no information on the comparability of different software versions. We assessed the feasibility, reproducibility and within patient temporal variability of 2DSTE in a large community based sample of older adults. We additionally compared 2DSTE results to those generated by 3DSTE. 1408 participants underwent transthoracic echocardiography. Using Philips Qlab 8.1 peak LV rotation at either the base or the apex was analysable in 432 (31%) participants. Peak twist measurements were achieved in 274 (20%) participants. 66 participants were randomly selected for the reproducibility study. 20 additional participants had scans 4-6 weeks apart for temporal variability and 3D echocardiography to assess the agreement between 2DSTE and 3DSTE. Reproducibility was evaluated using the intraclass coefficient of correlation (ICC). Better reproducibility for rotation and twist were obtained when measured at the endocardium, and when using more recent software versions, Peak twist and rotation were significantly different using two versions of the same software. Agreement with 3DSTE was better using newer software. Feasibility of 2DSTE is low in this cohort of elderly individuals severely limiting its utility in clinical settings. However if high quality images can be acquired assessment of ventricular rotation by 2DSTE is reproducible. Caution should be taken when comparing measurements of ventricular rotation by software from different vendors or different versions of software from the same vendor.

Point-pair registration is widely used in an image-guided neurosurgery system. Poor distribution of the fiducial points leads to an increase in the target registration error (TRE). This study aimed to provide templates consisting of optimized positioning of the fiducial points to reduce the TRE in image-guided neurosurgery. We divided the head into 6 regions and provided distribution templates of the fiducial points for each of them. A variable termed TREM(r) was used to express the approximate expected square of the TRE at the target point with a specified distribution of fiducial points. We randomly selected 85 patients from 5 hospitals who underwent image-guided neurosurgery and compared the TREM(r) of the real fiducial points with that of the templates. We grouped the patients by hospitals and regions. The mean TREM(r)s of the templates were much smaller than those of the real fiducial points. In each group, the range of the TREM(r) values of the templates was much smaller than that of the real fiducial points. This study provides an easy method to implement a good distribution of the fiducial points to help reduce TRE in image-guided neurosurgery. The templates are simple and exact and can be easily integrated into current workflow.

The Papanicolau (Pap) test is a routine cytological procedure for early detection of dysplastic lesions in cervical epithelium. A reliable screening method is crucial for triage of women at risk; however manual screening and interpretation are associated with relatively low sensitivity and substantial interobserver diagnostic variability. P16 and Ki67 biomarkers have been recently proposed as adjunctive tools in the diagnosis of high-risk human papillomavirus (hrHPV) associated dysplasias to supplement the morphological characteristics of cells by additional colorimetric features. In this study, an automated technique for the evaluation of dual p16/Ki67 immunoreactivity in cervical cell nuclei is introduced. Smears stained with p16 and Ki67 antibodies were digitized, and analyzed by algorithms we developed. Gradient-based radial symmetry operator and adaptive processing of symmetry image were employed to obtain the nuclear mask. This step was followed by the extraction of features including pixel data and immunoreactivity signature from each nucleus. The features were analyzed by two support vector machine classifiers to assign a nucleus into one of four types of immunoreactivity: p16 positive (p16 + /Ki67 − ), Ki67 positive (p16 − /Ki67 + ), dual p16/Ki67 positive (p16 + /Ki67 + ) and negative (p16 − /Ki67 − ), respectively. Results obtained by our method correlated well with readings by two cytopathologists ( n  = 18,068 cells); p16 + /Ki67 + nuclei were classified with respective precisions of 77.1% and 82.6%. Specificity in identification of p16 − /Ki67 − nuclei was better than 99.5%, and the sensitivity in detection of all immunopositive nuclei was 86.3 and 89.4%, respectively. We found that the quantitative characterization of immunoreactivity provided by the additional highlighting of classified nuclei can positively impact the efficacy and screening outcome of the Pap test.

Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained, and imaged to determine whether surgical margins are free of tumor cells—a costly and time- and labor-intensive procedure. Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning. The DeepDOF microscope consists of a conventional fluorescence microscope with the simple addition of an inexpensive (less than $10) phase mask inserted in the pupil plane to encode the light field and enhance the depth-invariance of the point-spread function. When used with a jointly optimized image-reconstruction algorithm, diffraction-limited optical performance to resolve subcellular features can be maintained while significantly extending the DOF (200 μm). Data from resected oral surgical specimens show that the DeepDOF microscope can consistently visualize nuclear morphology and other important diagnostic features across highly irregular resected tissue surfaces without serial refocusing. With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue sampling during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings.

Purpose Isolated zygomatic arch fractures (IZAFs) are habitually reduced at a distance, via a temporal approach. Open reductions are not recommended due to the associated morbidity and complications. However, performing closed reductions makes it difficult to determine whether it was done satisfactorily. This study aims to determine whether the acquisition of intraoperative images with a C-arm to evaluate IZAF reductions is a useful technique in treating such fractures. Methods Our hypothesis is that acquiring intraoperative images with a C-arm reduces the need for a second surgery. Between 2009 and 2012, 50 patients who were diagnosed with IZAF requiring surgery were randomly distributed into two groups: 25 patients were in the experimental group, where fracture reduction was performed and immediately corroborated intraoperatively for an adequate result using a C-arm, and 25 patients were assigned to a control group where the fracture reduction was controlled with post-surgery imaging. Results The results did not reveal significant differences between both groups ( p  = 0.5). Nevertheless, the experimental group had the advantage of being able to immediately reduce the fracture again if the result was unsatisfactory. Conclusions Despite the fact that the results are not statistically significant ( p  = 0.5), the authors recommend undertaking an intraoperative imaging analysis in areas where we are not certain of the reduction.

This Perspective introduces the development and use of adaptive optics in correcting aberrations in deep optical imaging applications. The past quarter century has witnessed rapid developments of fluorescence microscopy techniques that enable structural and functional imaging of biological specimens at unprecedented depth and resolution. The performance of these methods in multicellular organisms, however, is degraded by sample-induced optical aberrations. Here I review recent work on incorporating adaptive optics, a technology originally applied in astronomical telescopes to combat atmospheric aberrations, to improve image quality of fluorescence microscopy for biological imaging.

Surface-based cortical registration methods that are driven by geometrical features, such as folding, provide sub-optimal alignment of many functional areas due to variable correlation between cortical folding patterns and function. This has led to the proposal of new registration methods using features derived from functional and diffusion imaging. However, as yet there is no consensus over the best set of features for optimal alignment of brain function. In this paper we demonstrate the utility of a new Multimodal Surface Matching (MSM) algorithm capable of driving alignment using a wide variety of descriptors of brain architecture, function and connectivity. The versatility of the framework originates from adapting the discrete Markov Random Field (MRF) registration method to surface alignment. This has the benefit of being very flexible in the choice of a similarity measure and relatively insensitive to local minima. The method offers significant flexibility in the choice of feature set, and we demonstrate the advantages of this by performing registrations using univariate descriptors of surface curvature and myelination, multivariate feature sets derived from resting fMRI, and multimodal descriptors of surface curvature and myelination. We compare the results with two state of the art surface registration methods that use geometric features: FreeSurfer and Spherical Demons. In the future, the MSM technique will allow explorations into the best combinations of features and alignment strategies for inter-subject alignment of cortical functional areas for a wide range of neuroimaging data sets.

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s −1 without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology. High throughput imaging flow cytometry suffers from trade-offs between throughput, sensitivity and spatial resolution. Here the authors introduce a method to virtually freeze cells in the image acquisition window to enable 1000 times longer signal integration time and improve signal-to-noise ratio.

Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline, 3DeeCellTracker, by integrating multiple existing and new techniques including deep learning for tracking. With only one volume of training data, one initial correction, and a few parameter changes, 3DeeCellTracker successfully segmented and tracked ~100 cells in both semi-immobilized and ‘straightened’ freely moving worm's brain, in a naturally beating zebrafish heart, and ~1000 cells in a 3D cultured tumor spheroid. While these datasets were imaged with highly divergent optical systems, our method tracked 90–100% of the cells in most cases, which is comparable or superior to previous results. These results suggest that 3DeeCellTracker could pave the way for revealing dynamic cell activities in image datasets that have been difficult to analyze. Microscopes have been used to decrypt the tiny details of life since the 17th century. Now, the advent of 3D microscopy allows scientists to build up detailed pictures of living cells and tissues. In that effort, automation is becoming increasingly important so that scientists can analyze the resulting images and understand how bodies grow, heal and respond to changes such as drug therapies. In particular, algorithms can help to spot cells in the picture (called cell segmentation), and then to follow these cells over time across multiple images (known as cell tracking). However, performing these analyses on 3D images over a given period has been quite challenging. In addition, the algorithms that have already been created are often not user-friendly, and they can only be applied to a specific dataset gathered through a particular scientific method. As a response, Wen et al. developed a new program called 3DeeCellTracker, which runs on a desktop computer and uses a type of artificial intelligence known as deep learning to produce consistent results. Crucially, 3DeeCellTracker can be used to analyze various types of images taken using different types of cutting-edge microscope systems. And indeed, the algorithm was then harnessed to track the activity of nerve cells in moving microscopic worms, of beating heart cells in a young small fish, and of cancer cells grown in the lab. This versatile tool can now be used across biology, medical research and drug development to help monitor cell activities.