Automated microseismic event location using Master-Event Waveform Stacking.
Ontology highlight
ABSTRACT:
SUBMITTER: Grigoli F
PROVIDER: S-EPMC4868999 | biostudies-other | 2016
REPOSITORIES: biostudies-other
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Project description:PurposeThe purpose of this study was to investigate the utility of automated focal plane merging with the collection of gonio-photographs with different depths of field (DOF) using an established focus-stacking algorithm.MethodsA cross-sectional study was conducted at Shimane University Hospital, Izumo, Japan. Sixteen eyes from 16 subjects from the glaucoma clinic were included in this study. Image processing was performed for the images of 16 eyes from 16 angle sector following the successful gonio-photography. The 256 sets of focus-stacked and best-focused images were prepared in random order and were compared for the DOF and informativeness to diagnose angle pathology by masked observers in each set as the subjective assessments. Moreover, the energy of the Laplacian (average |ΔI|), which is an indicator of image sharpness between the photographs with and without the focus-stacking processing was also analyzed with the Laplacian filter as the objective assessment.ResultsThe automated image processing was successfully performed in all stacks of images. The significant deepening of DOF and improvement of informativeness achieved in 255 (99.6%) and 216 (84.4%) images (P < 0.0001 for both, sign test) and the energy of the Laplacian also significantly increased in 243 (94.9%) images (P < 0.0001, sign test).ConclusionsFocal plane merging by the automated algorithm can make the gonio-images deeper focus compared with the paired best-focused images subjectively and objectively, which would be useful for angle pathological assessment in clinical practice.Translational relevanceFocal plane merging algorithm for the automated gonio-photography can facilitate the angle assessment by providing informative deep-focus image, which would be useful for glaucoma care.
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Project description:A time-consuming challenge faced by camera trap practitioners is the extraction of meaningful data from images to inform ecological management. An increasingly popular solution is automated image classification software. However, most solutions are not sufficiently robust to be deployed on a large scale due to lack of location invariance when transferring models between sites. This prevents optimal use of ecological data resulting in significant expenditure of time and resources to annotate and retrain deep learning models.We present a method ecologists can use to develop optimized location invariant camera trap object detectors by (a) evaluating publicly available image datasets characterized by high intradataset variability in training deep learning models for camera trap object detection and (b) using small subsets of camera trap images to optimize models for high accuracy domain-specific applications.We collected and annotated three datasets of images of striped hyena, rhinoceros, and pigs, from the image-sharing websites FlickR and iNaturalist (FiN), to train three object detection models. We compared the performance of these models to that of three models trained on the Wildlife Conservation Society and Camera CATalogue datasets, when tested on out-of-sample Snapshot Serengeti datasets. We then increased FiN model robustness by infusing small subsets of camera trap images into training.In all experiments, the mean Average Precision (mAP) of the FiN trained models was significantly higher (82.33%-88.59%) than that achieved by the models trained only on camera trap datasets (38.5%-66.74%). Infusion further improved mAP by 1.78%-32.08%.Ecologists can use FiN images for training deep learning object detection solutions for camera trap image processing to develop location invariant, robust, out-of-the-box software. Models can be further optimized by infusion of 5%-10% camera trap images into training data. This would allow AI technologies to be deployed on a large scale in ecological applications. Datasets and code related to this study are open source and available on this repository: https://doi.org/10.5061/dryad.1c59zw3tx.
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Project description:BACKGROUND:In emergency settings, verification of endotracheal tube (ETT) location is important for critically ill patients. Ignorance of oesophageal intubation can be disastrous. Many methods are used for verification of the endotracheal tube location; none are ideal. Quantitative waveform capnography is considered the standard of care for this purpose but is not always available and is expensive. Therefore, this feasibility study is conducted to compare a cheaper alternative, bedside upper airway ultrasonography to waveform capnography, for verification of endotracheal tube location after intubation. METHODS:This was a prospective, single-centre, observational study, conducted at the HRPB, Ipoh. It included patients who were intubated in the emergency department from 28 March 2012 to 17 August 2012. A waiver of consent had been obtained from the Medical Research Ethics Committee. Bedside upper airway ultrasonography was performed after intubation and compared to waveform capnography. Specificity, sensitivity, positive and negative predictive value and likelihood ratio are calculated. RESULTS:A sample of 107 patients were analysed, and 6 (5.6%) had oesophageal intubations. The overall accuracy of bedside upper airway ultrasonography was 98.1% (95% confidence interval (CI) 93.0% to 100.0%). The kappa value (?) was 0.85, indicating a very good agreement between the bedside upper airway ultrasonography and waveform capnography. Thus, bedside upper airway ultrasonography is in concordance with waveform capnography. The sensitivity, specificity, positive predictive value and negative predictive value of bedside upper airway ultrasonography were 98.0% (95% CI 93.0% to 99.8%), 100% (95% CI 54.1% to 100.0%), 100% (95% CI 96.3% to 100.0%) and 75.0% (95% CI 34.9% to 96.8%). The likelihood ratio of a positive test is infinite and the likelihood ratio of a negative test is 0.0198 (95% CI 0.005 to 0.0781). The mean confirmation time by ultrasound is 16.4 s. No adverse effects were recorded. CONCLUSIONS:Our study shows that ultrasonography can replace waveform capnography in confirming ETT placement in centres without capnography. This can reduce incidence of unrecognised oesophageal intubation and prevent morbidity and mortality. TRIAL REGISTRATION:National Medical Research Register NMRR11100810230.
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