scholarly journals Automated description of the mandible shape by deep learning

2021 ◽  
Author(s):  
Nicolás Vila-Blanco ◽  
Paulina Varas-Quintana ◽  
Ángela Aneiros-Ardao ◽  
Inmaculada Tomás ◽  
María J. Carreira
Keyword(s):  
Large Scale ◽  
Imaging Techniques ◽  
Quantitative Description ◽  
Biological Information ◽  
Distribution Model ◽  
Anatomical Landmarks ◽  
Sample Collection ◽  
Point Distribution ◽  
Mandible Shape ◽  
Fully Automatic

Abstract Purpose The shape of the mandible has been analyzed in a variety of fields, whether to diagnose conditions like osteoporosis or osteomyelitis, in forensics, to estimate biological information such as age, gender, and race or in orthognathic surgery. Although the methods employed produce encouraging results, most rely on the dry bone analyses or complex imaging techniques that, ultimately, hamper sample collection and, as a consequence, the development of large-scale studies. Thus, we proposed an objective, repeatable, and fully automatic approach to provide a quantitative description of the mandible in orthopantomographies (OPGs). Methods We proposed the use of a deep convolutional neural network (CNN) to localize a set of landmarks of the mandible contour automatically from OPGs. Furthermore, we detailed four different descriptors for the mandible shape to be used for a variety of purposes. This includes a set of linear distances and angles calculated from eight anatomical landmarks of the mandible, the centroid size, the shape variations from the mean shape, and a group of shape parameters extracted with a point distribution model. Results The fully automatic digitization of the mandible contour was very accurate, with a mean point to the curve error of 0.21 mm and a standard deviation comparable to that of a trained expert. The combination of the CNN and the four shape descriptors was validated in the well-known problems of forensic sex and age estimation, obtaining 87.8% of accuracy and a mean absolute error of 1.57 years, respectively. Conclusion The methodology proposed, including the shape model, can be valuable in any field that requires a quantitative description of the mandible shape and a visual representation of its changes such as clinical practice, surgery management, dental research, or legal medicine.

A three-dimensional active shape model for the detection of anatomical landmarks on the back surface

2005 ◽  
Vol 219 (2) ◽  
pp. 129-142 ◽  
Author(s):  
T Huysmans ◽  
R Van Audekercke ◽  
J Vander Sloten ◽  
H Bruyninckx ◽  
G Van der Perre
Keyword(s):  
Mode Shapes ◽  
Distribution Model ◽  
Back Surface ◽  
Active Shape Model ◽  
Anatomical Landmarks ◽  
Shape Model ◽  
Point Distribution ◽  
Pelvic Inclination ◽  
Active Shape ◽  
Point Distribution Model

In this study relations between anatomical landmarks on the dorsal surface of the human torso corresponding to underlying skeletal structures are established. By examining the statistics of the positions of the landmarks in a training set of subjects a point distribution model is derived. Rotations of the pelvis are simulated in order to show that the main mode shapes of variation are consistent with rotations of the pelvis relative to the trunk. The parameters of these mode shapes can therefore be used as independent measures of clinical parameters such as pelvic inclination, pelvic tilt, etc. The point distribution model is further applied to improve reliability and robustness for an automatic and objective detection of the anatomical landmarks on the back surface (active shape model). The results show that it is possible to replace radiographs by surface measurements in order to measure position and orientation of the pelvis, which is particularly valuable in the case of functional examinations that normally involve a large number of radiographs (e.g. to measure the position of the pelvis in a scoliosis).

scholarly journals A generalised approach for high-throughput instance segmentation of stomata in microscope images

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Hiranya Jayakody ◽  
Paul Petrie ◽  
Hugo Jan de Boer ◽  
Mark Whitty
Keyword(s):  
High Throughput ◽  
Imaging Techniques ◽  
Detection Algorithm ◽  
Input Image ◽  
Detection Methods ◽  
Sample Collection ◽  
High Throughput Analysis ◽  
General Applicability ◽  
Additional Image ◽  
Bounding Boxes

Abstract Background Stomata analysis using microscope imagery provides important insight into plant physiology, health and the surrounding environmental conditions. Plant scientists are now able to conduct automated high-throughput analysis of stomata in microscope data, however, existing detection methods are sensitive to the appearance of stomata in the training images, thereby limiting general applicability. In addition, existing methods only generate bounding-boxes around detected stomata, which require users to implement additional image processing steps to study stomata morphology. In this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries regardless of the plant species, sample collection method, imaging technique and magnification level. Results The proposed solution consists of three stages. First, the input image is pre-processed to remove any colour space biases occurring from different sample collection and imaging techniques. Then, a Mask R-CNN is applied to estimate individual stomata boundaries. The feature pyramid network embedded in the Mask R-CNN is utilised to identify stomata at different scales. Finally, a statistical filter is implemented at the Mask R-CNN output to reduce the number of false positive generated by the network. The algorithm was tested using 16 datasets from 12 sources, containing over 60,000 stomata. For the first time in this domain, the proposed solution was tested against 7 microscope datasets never seen by the algorithm to show the generalisability of the solution. Results indicated that the proposed approach can detect stomata with a precision, recall, and F-score of 95.10%, 83.34%, and 88.61%, respectively. A separate test conducted by comparing estimated stomata boundary values with manually measured data showed that the proposed method has an IoU score of 0.70; a 7% improvement over the bounding-box approach. Conclusions The proposed method shows robust performance across multiple microscope image datasets of different quality and scale. This generalised stomata detection algorithm allows plant scientists to conduct stomata analysis whilst eliminating the need to re-label and re-train for each new dataset. The open-source code shared with this project can be directly deployed in Google Colab or any other Tensorflow environment.

scholarly journals Factors Enhancing Serum Syndecan-1 Concentrations: A Large-Scale Comprehensive Medical Examination

2019 ◽  
Vol 8 (9) ◽  
pp. 1320
Author(s):  
Kazumasa Oda ◽  
Hideshi Okada ◽  
Akio Suzuki ◽  
Hiroyuki Tomita ◽  
Ryo Kobayashi ◽  
...  
Keyword(s):  
Large Scale ◽  
Prospective Observational Study ◽  
Nitrogen Concentration ◽  
Laboratory Data ◽  
University Hospital ◽  
Endothelial Glycocalyx ◽  
Sample Collection ◽  
Blood Samples ◽  
Triglyceride Concentration ◽  
Medical Examinations

Endothelial disorders are related to various diseases. An initial endothelial injury is characterized by endothelial glycocalyx injury. We aimed to evaluate endothelial glycocalyx injury by measuring serum syndecan-1 concentrations in patients during comprehensive medical examinations. A single-center, prospective, observational study was conducted at Asahi University Hospital. The participants enrolled in this study were 1313 patients who underwent comprehensive medical examinations at Asahi University Hospital from January 2018 to June 2018. One patient undergoing hemodialysis was excluded from the study. At enrollment, blood samples were obtained, and study personnel collected demographic and clinical data. No treatments or exposures were conducted except for standard medical examinations and blood sample collection. Laboratory data were obtained by the collection of blood samples at the time of study enrolment. According to nonlinear regression, the concentrations of serum syndecan-1 were significantly related to age (p = 0.016), aspartic aminotransferase concentration (AST, p = 0.020), blood urea nitrogen concentration (BUN, p = 0.013), triglyceride concentration (p < 0.001), and hematocrit (p = 0.006). These relationships were independent associations. Endothelial glycocalyx injury, which is reflected by serum syndecan-1 concentrations, is related to age, hematocrit, AST concentration, BUN concentration, and triglyceride concentration.

Dynamic behavior of diffusion flame interacting with a large-scale vortex by laser imaging techniques

2005 ◽  
Vol 30 (1) ◽  
pp. 465-473 ◽  
Author(s):  
Masaharu Komiyama ◽  
Tomoya Fujimura ◽  
Toshimi Takagi ◽  
Shinichi Kinoshita
Keyword(s):  
Dynamic Behavior ◽  
Diffusion Flame ◽  
Large Scale ◽  
Imaging Techniques ◽  
Laser Imaging ◽  
Large Scale Vortex

scholarly journals Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice

2020 ◽  
Vol 17 (12) ◽  
pp. 4424
Author(s):  
Kuofeng Hung ◽  
Andy Wai Kan Yeung ◽  
Ray Tanaka ◽  
Michael M. Bornstein
Keyword(s):  
Treatment Planning ◽  
3D Imaging ◽  
Imaging Techniques ◽  
Current Trend ◽  
Three Dimensional ◽  
Image Data ◽  
Anatomical Landmarks ◽  
Automated Diagnosis ◽  
Dental Practitioners ◽  
Facial Scanning

The increasing use of three-dimensional (3D) imaging techniques in dental medicine has boosted the development and use of artificial intelligence (AI) systems for various clinical problems. Cone beam computed tomography (CBCT) and intraoral/facial scans are potential sources of image data to develop 3D image-based AI systems for automated diagnosis, treatment planning, and prediction of treatment outcome. This review focuses on current developments and performance of AI for 3D imaging in dentomaxillofacial radiology (DMFR) as well as intraoral and facial scanning. In DMFR, machine learning-based algorithms proposed in the literature focus on three main applications, including automated diagnosis of dental and maxillofacial diseases, localization of anatomical landmarks for orthodontic and orthognathic treatment planning, and general improvement of image quality. Automatic recognition of teeth and diagnosis of facial deformations using AI systems based on intraoral and facial scanning will very likely be a field of increased interest in the future. The review is aimed at providing dental practitioners and interested colleagues in healthcare with a comprehensive understanding of the current trend of AI developments in the field of 3D imaging in dental medicine.

scholarly journals Assessing the measurement transfer function of single-cell RNA sequencing

2016 ◽  
Author(s):  
Hannah R. Dueck ◽  
Rizi Ai ◽  
Adrian Camarena ◽  
Bo Ding ◽  
Reymundo Dominguez ◽  
...  
Keyword(s):  
Transfer Function ◽  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Transfer Functions ◽  
Single Cells ◽  
Biological Information ◽  
Gene Detection ◽  
Single Cell Rna Sequencing ◽  
Scale Control

AbstractRecently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known. Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate the measurement transfer functions to be linear above ~5-10 molecules. Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

scholarly journals Leucistic Woodcock (Scolopax rusticola L.) occurrences in Hungary from the second half of the 19th century to the present day

2019 ◽  
Vol 27 (2) ◽  
pp. 100-114
Author(s):  
Attila Bende ◽  
Angéla Király ◽  
Richárd lászló
Keyword(s):  
Large Scale ◽  
Sample Collection ◽  
Hungarian Literature ◽  
Scolopax Rusticola ◽  
Large Scale Analysis ◽  
Genetic Abnormalities ◽  
Before And After ◽  
Colour Changes ◽  
The 19Th Century ◽  
White Colour

Abstract Publications about curiosities are known in the Hungarian and international ornithological literature since the 1800s. Although studies explaining the processes of pigmentation dysfunctions have been known since the mid-nineteenth century, these specimens still appear only as curiosities in the professional press and the terminology used to specify them is generally incorrect. The analysed genetic abnormalities causing white colour varieties in Woodcock (albinism, leucism, Ino) are due to mutations. By briefly describing the biological background of the defects, this work helps detect colour changes. In this article, we provide a broad overview of partially or completely white Woodcocks (n = 23 expl.) found in international (8 countries) and Hungarian literature. We have supplemented the literature background with our own studies. The large-scale analysis of the variability of colours and patterns was made possible by the countrywide wing sample collection within the biometric module of Woodcock Monitoring, which has been running under the coordination of the Hungarian Hunting Conservation Association since 2010. Within this framework, 12,078 samples were analysed between 2010–2018. We found that pigment deficiency occurred in the sample set only with a proportion of 0.01%. Based on the Hungarian literature and our own samples, we presented the known occurrences on maps of the state territory with boundaries before and after 1921, indicating the causes of patterns of occurrence by migration and frequencies of occurrence.

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