Classification and numbering of teeth in multi-slice CT images using wavelet-Fourier descriptor

Hosntalab, Mohammad; Zoroofi, Reza Aghaeizadeh; Tehrani‐Fard, Ali Abbaspour; Shirani, Gholamreza

doi:10.1007/s11548-009-0389-8

Cited by 30 publications

(18 citation statements)

References 39 publications

(65 reference statements)

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“…A machine learning algorithm predicts the disease/species from these features. Munisami et al (2015) segmented leaf images with Otsu’s algorithm ( Baxi and Vala, 2013 ) and calculated leaf length, width, area, perimeter, hull area, axis length and centroid and applied a K-NN for prediction. The system was tested on 640 images of 32 different plant species.…”

Section: Introductionmentioning

confidence: 99%

X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion

et al. 2017

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We have developed a vision-based program to detect symptoms of Olive Quick Decline Syndrome (OQDS) on leaves of Olea europaea L. infected by Xylella fastidiosa, named X-FIDO (Xylella FastIdiosa Detector for O. europaea L.). Previous work predicted disease from leaf images with deep learning but required a vast amount of data which was obtained via crowd sourcing such as the PlantVillage project. This approach has limited applicability when samples need to be tested with traditional methods (i.e., PCR) to avoid incorrect training input or for quarantine pests which manipulation is restricted. In this paper, we demonstrate that transfer learning can be leveraged when it is not possible to collect thousands of new leaf images. Transfer learning is the re-application of an already trained deep learner to a new problem. We present a novel algorithm for fusing data at different levels of abstraction to improve performance of the system. The algorithm discovers low-level features from raw data to automatically detect veins and colors that lead to symptomatic leaves. The experiment included images of 100 healthy leaves, 99 X. fastidiosa-positive leaves and 100 X. fastidiosa-negative leaves with symptoms related to other stress factors (i.e., abiotic factors such as water stress or others diseases). The program detects OQDS with a true positive rate of 98.60 ± 1.47% in testing, showing great potential for image analysis for this disease. Results were obtained with a convolutional neural network trained with the stochastic gradient descent method, and ten trials with a 75/25 split of training and testing data. This work shows potential for massive screening of plants with reduced diagnosis time and cost.

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Section: Introductionmentioning

confidence: 99%

X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion

et al. 2017

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“…For teeth detection, Lin et. al 4 and Hosntalab et al 5 proposed pixel-level segmentation methods based on traditional computer vision techniques, such as thresholding, histogram-based, and level set methods. They detected teeth with the recall (sensitivity) of 0.94 and 0.88 respectively.…”

mentioning

confidence: 99%

Tooth detection and numbering in panoramic radiographs using convolutional neural networks

Tuzoff

Tuzova²,

Bornstein

et al. 2019

Dentomaxillofacial Radiology

252

122

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Objectives: Analysis of dental radiographs is an important part of the diagnostic process in daily clinical practice. Interpretation by an expert includes teeth detection and numbering. In this project, a novel solution based on convolutional neural networks (CNNs) is proposed that performs this task automatically for panoramic radiographs. Methods: A data set of 1352 randomly chosen panoramic radiographs of adults was used to train the system. The CNN-based architectures for both teeth detection and numbering tasks were analyzed. The teeth detection module processes the radiograph to define the boundaries of each tooth. It is based on the state-of-the-art Faster R-CNN architecture. The teeth numbering module classifies detected teeth images according to the FDI notation. It utilizes the classical VGG-16 CNN together with the heuristic algorithm to improve results according to the rules for spatial arrangement of teeth. A separate testing set of 222 images was used to evaluate the performance of the system and to compare it to the expert level. Results: For the teeth detection task, the system achieves the following performance metrics: a sensitivity of 0.9941 and a precision of 0.9945. For teeth numbering, its sensitivity is 0.9800 and specificity is 0.9994. Experts detect teeth with a sensitivity of 0.9980 and a precision of 0.9998. Their sensitivity for tooth numbering is 0.9893 and specificity is 0.9997. The detailed error analysis showed that the developed software system makes errors caused by similar factors as those for experts. conclusions: The performance of the proposed computer-aided diagnosis solution is comparable to the level of experts. Based on these findings, the method has the potential for practical application and further evaluation for automated dental radiograph analysis. Computer-aided teeth detection and numbering simplifies the process of filling out digital dental charts. Automation could help to save time and improve the completeness of electronic dental records.

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“…Image analysis was performed in ImageJ (NIH -National Institutes of Health) and MATLAB. For analysis of cell body and nuclei, images were split into individual channels from RGB (red 594 nm and green 488 nm, blue 350 nm) and thresholded utilizing the Otsu method (Vala and Baxi, 2013) to remove any uneven illumination. Any debris and unwanted artifacts were cropped from the image.…”

Section: Image Acquisition and Analysis Quantification Of Marker Posmentioning

confidence: 99%

Fast and Efficient Differentiation of Mouse Embryonic Stem Cells Into ATP-Responsive Astrocytes

Juneja

Nasuto

Delivopoulos

2020

Front. Cell. Neurosci.

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Astrocytes are multifunctional cells in the CNS, involved in the regulation of neurovascular coupling, the modulation of electrolytes, and the cycling of neurotransmitters at synapses. Induction of astrocytes from stem cells remains a largely underdeveloped area, as current protocols are time consuming, lack granularity in astrocytic subtype generation, and often are not as efficient as neural induction methods. In this paper we present an efficient method to differentiate astrocytes from mouse embryonic stem cells. Our technique uses a cell suspension protocol to produce embryoid bodies (EBs) that are neurally inducted and seeded onto laminin coated surfaces. Plated EBs attach to the surface and release migrating cells to their surrounding environment, which are further inducted into the astrocytic lineage, through an optimized, heparin-based media. Characterization and functional assessment of the cells consists of immunofluorescent labeling for specific astrocytic proteins and sensitivity to adenosine triphosphate (ATP) stimulation. Our experimental results show that even at the earliest stages of the protocol, cells are positive for astrocytic markers (GFAP, ALDH1L1, S100β, and GLAST) with variant expression patterns and purinergic receptors (P2Y). Generated astrocytes also exhibit differential Ca 2+ transients upon stimulation with ATP, which evolve over the differentiation period. Metabotropic purinoceptors P2Y 1 R are expressed and we offer preliminary evidence that metabotropic purinoceptors contribute to Ca 2+ transients. Our protocol is simple, efficient and fast, facilitating its use in multiple investigations, particularly in vitro studies of engineered neural networks.

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Classification and numbering of teeth in multi-slice CT images using wavelet-Fourier descriptor

Cited by 30 publications

References 39 publications

X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion

X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion

Tooth detection and numbering in panoramic radiographs using convolutional neural networks

Fast and Efficient Differentiation of Mouse Embryonic Stem Cells Into ATP-Responsive Astrocytes

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