3,779 research outputs found
Mesh-to-raster based non-rigid registration of multi-modal images
Region of interest (ROI) alignment in medical images plays a crucial role in
diagnostics, procedure planning, treatment, and follow-up. Frequently, a model
is represented as triangulated mesh while the patient data is provided from CAT
scanners as pixel or voxel data. Previously, we presented a 2D method for
curve-to-pixel registration. This paper contributes (i) a general
mesh-to-raster (M2R) framework to register ROIs in multi-modal images; (ii) a
3D surface-to-voxel application, and (iii) a comprehensive quantitative
evaluation in 2D using ground truth provided by the simultaneous truth and
performance level estimation (STAPLE) method. The registration is formulated as
a minimization problem where the objective consists of a data term, which
involves the signed distance function of the ROI from the reference image, and
a higher order elastic regularizer for the deformation. The evaluation is based
on quantitative light-induced fluoroscopy (QLF) and digital photography (DP) of
decalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, each
showing one corresponding tooth in both modalities. The ROI in each image is
manually marked by three experts (900 curves in total). In the QLF-DP setting,
our approach significantly outperforms the mutual information-based
registration algorithm implemented with the Insight Segmentation and
Registration Toolkit (ITK) and Elastix
A Statistical Modeling Approach to Computer-Aided Quantification of Dental Biofilm
Biofilm is a formation of microbial material on tooth substrata. Several
methods to quantify dental biofilm coverage have recently been reported in the
literature, but at best they provide a semi-automated approach to
quantification with significant input from a human grader that comes with the
graders bias of what are foreground, background, biofilm, and tooth.
Additionally, human assessment indices limit the resolution of the
quantification scale; most commercial scales use five levels of quantification
for biofilm coverage (0%, 25%, 50%, 75%, and 100%). On the other hand, current
state-of-the-art techniques in automatic plaque quantification fail to make
their way into practical applications owing to their inability to incorporate
human input to handle misclassifications. This paper proposes a new interactive
method for biofilm quantification in Quantitative light-induced fluorescence
(QLF) images of canine teeth that is independent of the perceptual bias of the
grader. The method partitions a QLF image into segments of uniform texture and
intensity called superpixels; every superpixel is statistically modeled as a
realization of a single 2D Gaussian Markov random field (GMRF) whose parameters
are estimated; the superpixel is then assigned to one of three classes
(background, biofilm, tooth substratum) based on the training set of data. The
quantification results show a high degree of consistency and precision. At the
same time, the proposed method gives pathologists full control to post-process
the automatic quantification by flipping misclassified superpixels to a
different state (background, tooth, biofilm) with a single click, providing
greater usability than simply marking the boundaries of biofilm and tooth as
done by current state-of-the-art methods.Comment: 10 pages, 7 figures, Journal of Biomedical and Health Informatics
2014. keywords: {Biomedical imaging;Calibration;Dentistry;Estimation;Image
segmentation;Manuals;Teeth},
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6758338&isnumber=636350
BiofilmQuant: A Computer-Assisted Tool for Dental Biofilm Quantification
Dental biofilm is the deposition of microbial material over a tooth
substratum. Several methods have recently been reported in the literature for
biofilm quantification; however, at best they provide a barely automated
solution requiring significant input needed from the human expert. On the
contrary, state-of-the-art automatic biofilm methods fail to make their way
into clinical practice because of the lack of effective mechanism to
incorporate human input to handle praxis or misclassified regions. Manual
delineation, the current gold standard, is time consuming and subject to expert
bias. In this paper, we introduce a new semi-automated software tool,
BiofilmQuant, for dental biofilm quantification in quantitative light-induced
fluorescence (QLF) images. The software uses a robust statistical modeling
approach to automatically segment the QLF image into three classes (background,
biofilm, and tooth substratum) based on the training data. This initial
segmentation has shown a high degree of consistency and precision on more than
200 test QLF dental scans. Further, the proposed software provides the
clinicians full control to fix any misclassified areas using a single click. In
addition, BiofilmQuant also provides a complete solution for the longitudinal
quantitative analysis of biofilm of the full set of teeth, providing greater
ease of usability.Comment: 4 pages, 4 figures, 36th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society (EMBC 2014
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
Applications of artificial intelligence in dentistry: A comprehensive review
This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Projects RTI2018-101674-B-I00 and PGC2018-101904-A-100, University of Granada project A.TEP. 280.UGR18, I+D+I Junta de Andalucia 2020 project P20-00200, and Fapergs/Capes do Brasil grant 19/25510000928-3. Funding for open-access charge: Universidad de Granada/CBUAObjective: To perform a comprehensive review of the use of artificial intelligence
(AI) and machine learning (ML) in dentistry, providing the community with a broad
insight on the different advances that these technologies and tools have produced,
paying special attention to the area of esthetic dentistry and color research.
Materials and methods: The comprehensive review was conducted in MEDLINE/
PubMed, Web of Science, and Scopus databases, for papers published in English language
in the last 20 years.
Results: Out of 3871 eligible papers, 120 were included for final appraisal. Study
methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other
ML techniques (n = 32), which were mainly applied to disease identification, image
segmentation, image correction, and biomimetic color analysis and modeling.
Conclusions: The insight provided by the present work has reported outstanding
results in the design of high-performance decision support systems for the aforementioned
areas. The future of digital dentistry goes through the design of integrated
approaches providing personalized treatments to patients. In addition, esthetic dentistry
can benefit from those advances by developing models allowing a complete
characterization of tooth color, enhancing the accuracy of dental restorations.
Clinical significance: The use of AI and ML has an increasing impact on the dental
profession and is complementing the development of digital technologies and tools,
with a wide application in treatment planning and esthetic dentistry procedures.Spanish Ministry of Sciences, Innovation and Universities RTI2018-101674-B-I00
PGC2018-101904-A-100University of Granada project A.TEP. 280.UGR18Junta de Andalucia P20-00200Fapergs/Capes do Brasil grant 19/25510000928-3Universidad de Granada/CBU
SEPARATION OF OVERLAPPING OBJECT SEGMENTATION USING LEVEL SET WITH AUTOMATIC INITALIZATION ON DENTAL PANORAMIC RADIOGRAPH
To extract features on dental objects, it is necessary to segment the teeth. Segmentation is separating between the teeth (objects) with another part than teeth (background). The process of segmenting individual teeth has done a lot of the recently research and obtained good results. However, when faced with overlapping teeth, this is quite challenging. Overlapping tooth segmentation using the latest algorithm produces an object that should be segmented into two objects, instantly becoming one object. This is due to the overlapping between two teeth. To separate overlapping teeth, it is necessary to extract the overlapping object first. Level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. In this study, an automatic initialization strategy is proposed for the level set method to segment overlapping teeth using hierarchical cluster analysis on dental panoramic radiographs images. The proposed strategy was able to initialize overlapping objects properly with accuracy of 73%. Evaluation to measure quality of segmentation result are using misscassification error (ME) and relative foreground area error (RAE). ME and RAE were calculated based on the average results of individual tooth segmentation and obtain 16.41% and 52.14%, respectively. This proposed strategy are expected to be able to help separate the overlapping teeth for human age estimation through dental images in forensic odontology
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