Deep learning for automated detection and numbering of permanent teeth on panoramic images

2021 ◽  
Author(s):  
Mohamed Estai ◽  
Marc Tennant ◽  
Dieter Gebauer ◽  
Andrew Brostek ◽  
Janardhan Vignarajan ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Detection System ◽  
Ground Truth ◽  
Automated Detection ◽  
Permanent Teeth ◽  
Automated Method ◽  
Step Procedure ◽  
Panoramic Images ◽  
Tooth Number

Objective: This study aimed to evaluate an automated detection system to detect and classify permanent teeth on orthopantomogram (OPG) images using convolutional neural networks (CNNs). Methods: In total, 591 digital OPGs were collected from patients older than 18 years. Three qualified dentists performed individual teeth labelling on images to generate the ground truth annotations. A three-step procedure, relying upon CNNs, was proposed for automated detection and classification of teeth. Firstly, U-Net, a type of CNN, performed preliminary segmentation of tooth regions or detecting regions of interest (ROIs) on panoramic images. Secondly, the Faster R-CNN, an advanced object detection architecture, identified each tooth within the ROI determined by the U-Net. Thirdly, VGG-16 architecture classified each tooth into 32 categories, and a tooth number was assigned. A total of 17,135 teeth cropped from 591 radiographs were used to train and validate the tooth detection and tooth numbering modules. 90% of OPG images were used for training, and the remaining 10% were used for validation. 10-folds cross-validation was performed for measuring the performance. The intersection over union (IoU), F1 score, precision, and recall (i.e. sensitivity) were used as metrics to evaluate the performance of resultant CNNs. Results: The ROI detection module had an IoU of 0.70. The tooth detection module achieved a recall of 0.99 and a precision of 0.99. The tooth numbering module had a recall, precision and F1 score of 0.98. Conclusion: The resultant automated method achieved high performance for automated tooth detection and numbering from OPG images. Deep learning can be helpful in the automatic filing of dental charts in general dentistry and forensic medicine.

scholarly journals Deep Learning for Automated Detection and Identification of Migrating American Eel Anguilla rostrata from Imaging Sonar Data

2021 ◽  
Vol 13 (14) ◽  
pp. 2671
Author(s):  
Xiaoqin Zang ◽  
Tianzhixi Yin ◽  
Zhangshuan Hou ◽  
Robert P. Mueller ◽  
Zhiqun Daniel Deng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fish Passage ◽  
Automated Detection ◽  
Anguilla Rostrata ◽  
Automated Method ◽  
Detection And Identification ◽  
And Migration ◽  
Imaging Sonar ◽  
American Eels ◽  
Deep Learning Model

Adult American eels (Anguilla rostrata) are vulnerable to hydropower turbine mortality during outmigration from growth habitat in inland waters to the ocean where they spawn. Imaging sonar is a reliable and proven technology for monitoring of fish passage and migration; however, there is no efficient automated method for eel detection. We designed a deep learning model for automated detection of adult American eels from sonar data. The method employs convolution neural network (CNN) to distinguish between 14 images of eels and non-eel objects. Prior to image classification with CNN, background subtraction and wavelet denoising were applied to enhance sonar images. The CNN model was first trained and tested on data obtained from a laboratory experiment, which yielded overall accuracies of >98% for image-based classification. Then, the model was trained and tested on field data that were obtained near the Iroquois Dam located on the St. Lawrence River; the accuracy achieved was commensurate with that of human experts.

High-Performance Virus Detection System by using Deep Learning

2020 ◽  
Author(s):  
Ying-Feng Hsu ◽  
Makiko Ito ◽  
Takumi Maruyama ◽  
Morito Matsuoka ◽  
Nicolas Jung ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Detection System ◽  
Virus Detection

scholarly journals Deep learning-based Helicobacter pylori detection: A diagnostic pathology study

2020 ◽  
Author(s):  
Sharon Zhou ◽  
Henrik Marklund ◽  
Ondrej Blaha ◽  
Manisha Desai ◽  
Brock Martin ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Characteristic Curve ◽  
Ground Truth ◽  
Turnaround Time ◽  
Great Promise ◽  
Test Set ◽  
Diagnostic Uncertainty ◽  
Gastric Biopsies ◽  
H Pylori

Aims: Deep learning (DL), a sub-area of artificial intelligence, has demonstrated great promise at automating diagnostic tasks in pathology, yet its translation into clinical settings has been slow. Few studies have examined its impact on pathologist performance, when embedded into clinical workflows. The identification of H. pylori on H&E stain is a tedious, imprecise task which might benefit from DL assistance. Here, we developed a DL assistant for diagnosing H. pylori in gastric biopsies and tested its impact on pathologist diagnostic accuracy and turnaround time. Methods and results: H&E-stained whole-slide images (WSI) of 303 gastric biopsies with ground truth confirmation by immunohistochemistry formed the study dataset; 47 and 126 WSI were respectively used to train and optimize our DL assistant to detect H. pylori, and 130 were used in a clinical experiment in which 3 experienced GI pathologists reviewed the same test set with and without assistance. On the test set, the assistant achieved high performance, with a WSI-level area-under-the-receiver-operating-characteristic curve (AUROC) of 0.965 (95% CI 0.934-0.987). On H. pylori-positive cases, assisted diagnoses were faster (β, the fixed effect size for assistance= -0.557, p=0.003) and much more accurate (OR=13.37, p<0.001) than unassisted diagnoses. However, assistance increased diagnostic uncertainty on H. pylori-negative cases, resulting in an overall decrease in assisted accuracy (OR=0.435, p=0.016) and negligible impact on overall turnaround time (β for assistance=0.010, p=0.860). Conclusions: DL can assist pathologists with H. pylori diagnosis, but its integration into clinical workflows requires optimization to mitigate diagnostic uncertainty as a potential consequence of assistance.

Abstract P379: Automatic Segmentation of Atherosclerotic Plaques in Transverse Carotid Ultrasound Images Using Deep Learning

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Leonard L Yeo ◽  
Melih Engin ◽  
Robin Lange ◽  
David Tang ◽  
Andras Nemes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Carotid Artery ◽  
Vessel Wall ◽  
Learning Algorithms ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Ultrasound Images ◽  
Carotid Ultrasound ◽  
Accurate Analysis ◽  
Automated Method

Purpose: Ultrasound imaging is commonly used for patients with atheroscelerotic plaques in the carotid artery. While B-mode ultrasound can be used for detection and measurement of these plaques, interpreting these images can be a subjective and time-consuming task. Deep learning algorithms have been proven to be an effective tool for interpreting medical images, especially for classification and segmentation tasks. Here, we propose a deep learning model to automatically detect and measure plaques in transverse B-mode images of the carotid artery. Methods: The proposed automated method takes a transverse B-mode image of the carotid artery as an input and segments the vessel wall in the transverse cross section image using convolutional neural networks. To ensure that the method can perform well in clinical settings, the method has been evaluated on not only healthy subjects (max IMT below 1.2 mm) but also on patients with atheroscelerotic plaques and other vessel wall pathology. Given the B-mode transverse image as an input, the neural network first finds a region of interest (ROI) surrounding the artery and then segments both the inner and outer wall of the carotid artery. We determined the accuracy of the system by F1 Score, a common metric to evaluate the performance of machine learning algorithms. Results: The network was trained and tested on a transverse ultrasound carotid artery dataset that has 506 images, gathered from 4 hospitals. Annotations of an expert reader were used as the ground truth and the performance of the method was evaluated using 5-fold cross validation. The proposed method reaches an F1 score of 0.91 for correctly detecting the ROI and an F1 score of 0.78 for detecting and segmenting the vessel walls in transverse B-mode images. Conclusions: The results show that the proposed deep learning method can be used for accurate analysis and interpretation of carotid ultrasound scans in a clinical setting and potentially reduce the reporting time while increasing objectivity of the analysis.

Deep Learning Assisted Burn Wound Diagnosis (Preprint)

2020 ◽  
Author(s):  
Che Wei Chang ◽  
Feipei Lai ◽  
Mesakh Christian ◽  
Yu Chun Chen ◽  
Ching Hsu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Body Surface Area ◽  
Ground Truth ◽  
Burn Patients ◽  
Burn Wound ◽  
Burn Wounds ◽  
Burn Victims ◽  
Step Procedure ◽  
Resuscitation Fluid ◽  
Total Body

BACKGROUND Accurate assessment of the percentage of total body surface area (%TBSA) of burn wounds is crucial in the management of burn patients. The resuscitation fluid and nutritional needs of burn patients, their need for intensive unit care, and probability of mortality are all directly related to %TBSA. It is difficult to estimate a burn area of irregular shape by inspection. Many articles have reported discrepancy in estimating %TBSA by different doctors. OBJECTIVE We propose a method, based on deep learning, for burn wound detection, segmentation and calculation of % TBSA on a pixel-to-pixel basis. METHODS A two-step procedure was used to convert burn wound diagnosis into %TBSA. In the first step, images of burn wounds were collected and labeled by burn surgeons and the dataset was then input into two deep learning architectures, U-Net and Mask R-CNN, each configured with two different backbones, to segment the burn wounds. In the second step, we collected and labeled images of hands to create another dataset, which was also input into U-Net and Mask R-CNN to segment the hands. The percentage of TBSA of the burn wounds was then calculated by comparing the pixels of mask areas on the images of the burn wound and hand of the same patient according to the rule of hand, which says that one’s hand accounts for 0.8% of TBSA. RESULTS A total of 2591 images of burn wounds were collected and labeled to form the burn-wound dataset. The dataset was randomly split into a ratio of 8:1:1 to form the training, validation, and testing sets. Four hundred images of volar hands were collected and labeled to form the hand dataset, which was also split into three sets using the same method. For the images of burn wounds, Mask R-CNN with ResNet101 had the best segmentation result with a Dice coefficient (DC) of 0.9496, while U-Net with ResNet101 had a DC of 0.8545. For the hand images, U-Net and Mask R-CNN had similar performance with a DC of 0.9920 and 0.9910, respectively. Lastly, we conducted a test diagnosis in a burn patient. Mask R-CNN with ResNet-101 had on average less deviation (0.115% TBSA) from the ground truth than burn surgeons. CONCLUSIONS This is one of the first studies to diagnose all depths of burn wounds and convert the segmentation results into %TBSA using different deep learning models. We aimed to assist medical staff in estimating burn size more accurately and thereby helping to provide precise care to burn victims.

scholarly journals Dark blood ischemic LGE segmentation using a deep learning approach

2021 ◽  
Vol 22 (Supplement_2) ◽  
Author(s):  
C Torlasco ◽  
D Papetti ◽  
R Mene ◽  
J Artico ◽  
A Seraphim ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Performance ◽  
Clinical Decision Making ◽  
Data Augmentation ◽  
Clinical Decision ◽  
Learning Approach ◽  
Dark Blood ◽  
Data Set ◽  
Automated Method

Abstract Funding Acknowledgements Type of funding sources: None. Introduction The extent of ischemic scar detected by Cardiac Magnetic Resonance (CMR) with late gadolinium enhancement (LGE) is linked with long-term prognosis, but scar quantification is time-consuming. Deep Learning (DL) approaches appear promising in CMR segmentation.  Purpose: To train and apply a deep learning approach to dark blood (DB) CMR-LGE for ischemic scar segmentation, comparing results to 4-Standard Deviation (4-SD) semi-automated method. Methods: We trained and validated a dual neural network infrastructure on a dataset of DB-LGE short-axis stacks, acquired at 1.5T from 33 patients with ischemic scar. The DL architectures were an evolution of the U-Net Convolutional Neural Network (CNN), using data augmentation to increase generalization. The CNNs worked together to identify and segment 1) the myocardium and 2) areas of LGE. The first CNN simultaneously cropped the region of interest (RoI) according to the bounding box of the heart and calculated the area of myocardium. The cropped RoI was then processed by the second CNN, which identified the overall LGE area. The extent of scar was calculated as the ratio of the two areas. For comparison, endo- and epi-cardial borders were manually contoured and scars segmented by a 4-SD technique with a validated software. Results: The two U-Net networks were implemented with two free and open-source software library for machine learning. We performed 5-fold cross-validation over a dataset of 108 and 385 labelled CMR images of the myocardium and scar, respectively. We obtained high performance (&gt; ∼0.85) as measured by the Intersection over Union metric (IoU) on the training sets, in the case of scar segmentation. With regards to heart recognition, the performance was lower (&gt; ∼0.7), although improved (∼ 0.75) by detecting the cardiac area instead of heart boundaries. On the validation set, performances oscillated between 0.8 and 0.85 for scar tissue recognition, and dropped to ∼0.7 for myocardium segmentation. We believe that underrepresented samples and noise might be affecting the overall performances, so that additional data might be beneficial. Figure1: examples of heart segmentation (upper left panel: training; upper right panel: validation) and of scar segmentation (lower left panel: training; lower right panel: validation). Conclusion: Our CNNs show promising results in automatically segmenting LV and quantify ischemic scars on DB-LGE-CMR images. The performances of our method can further improve by expanding the data set used for the training. If implemented in a clinical routine, this process can speed up the CMR analysis process and aid in the clinical decision-making. Abstract Figure.

scholarly journals A Deep Learning Approach to Detect Pornography Videos in Educational Repositories

2020 ◽  
Author(s):  
Pedro V. A. de Freitas ◽  
Antonio J. G. Busson ◽  
Álan L. V. Guedes ◽  
Sérgio Colcher
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Automated Detection ◽  
Learning Approach ◽  
Multimodal Approach ◽  
Deep Convolutional Neural Networks ◽  
High Level

A large number of videos are uploaded on educational platforms every minute. Those platforms are responsible for any sensitive media uploaded by their users. An automated detection system to identify pornographic content could assist human workers by pre-selecting suspicious videos. In this paper, we propose a multimodal approach to adult content detection. We use two Deep Convolutional Neural Networks to extract high-level features from both image and audio sources of a video. Then, we concatenate those features and evaluate the performance of classifiers on a set of mixed educational and pornographic videos. We achieve an F1-score of 95.67% on the educational and adult videos set and an F1-score of 94% on our test subset for the pornographic class.

scholarly journals High-Performance Deep learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Yaser AbdulAali Jasim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
Plant Disease ◽  
Detection System ◽  
Open Data ◽  
Data Set ◽  
Performance Rate

Nowadays, technology and computer science are rapidly developing many tools and algorithms, especially in the field of artificial intelligence.  Machine learning is involved in the development of new methodologies and models that have become a novel machine learning area of applications for artificial intelligence. In addition to the architectures of conventional neural network methodologies, deep learning refers to the use of artificial neural network architectures which include multiple processing layers. In this paper, models of the Convolutional neural network were designed to detect (diagnose) plant disorders by applying samples of healthy and unhealthy plant images analyzed by means of methods of deep learning. The models were trained using an open data set containing (18,000) images of ten different plants, including healthy plants. Several model architectures have been trained to achieve the best performance of (97 percent) when the respectively [plant, disease] paired are detected. This is a very useful information or early warning technique and a method that can be further improved with the substantially high-performance rate to support an automated plant disease detection system to work in actual farm conditions.

scholarly journals Development of a High-Performance Multiparametric MRI Oropharyngeal Primary Tumor Auto-Segmentation Deep Learning Model and Investigation of Input Channel Effects: Results from a Prospective Imaging Registry

2021 ◽  
Author(s):  
Kareem Wahid ◽  
Sara Ahmed ◽  
Renjie He ◽  
Lisanne van Dijk ◽  
Jonas Teuwen ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
False Negative ◽  
Model Performance ◽  
Ground Truth ◽  
Turing Test ◽  
Multiparametric Mri ◽  
Dice Similarity Coefficient ◽  
Channel Effects ◽  
The Impact

Background and Purpose: Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we constructed mpMRI deep learning (DL) OPC GTVp auto-segmentation models and determined the impact of input channels on segmentation performance. Materials and Methods: GTVp ground truth segmentations were manually generated for 30 OPC patients from a clinical trial. We evaluated five mpMRI input channels (T2, T1, ADC, Ktrans, Ve). 3D Residual U-net models were developed and assessed using leave-one-out cross-validation. A baseline T2 model was compared to mpMRI models (T2+T1, T2+ADC, T2+Ktrans, T2+Ve, all 5 channels [ALL]) primarily using the Dice similarity coefficient (DSC). Sensitivity, positive predictive value, Hausdorff distance (HD), false-negative DSC (FND), false-positive DSC, surface DSC, 95% HD, and mean surface distance were also assessed. For the best model, ground truth and DL-generated segmentations were compared through a Turing test using physician observers. Results: Models yielded mean DSCs from 0.71 (ALL) to 0.73 (T2+T1). Compared to the T2 model, performance was significantly improved for HD, FND, sensitivity, surface DSC, and 95% HD for the T2+T1 model (p<0.05) and for FND for the T2+Ve and ALL models (p<0.05). There were no differences between ground truth and DL-generated segmentations for all observers (p>0.05). Conclusion: DL using mpMRI provides high-quality segmentations of OPC GTVp. Incorporating additional mpMRI channels may increase the performance of certain evaluation metrics. This pilot study is a promising step towards fully automated MR-guided OPC radiotherapy.

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