TMDU Classification of Myopic Traction Maculopathy Based on OCT and Ultra Wide-Field OCT (UWF-OCT)

2020 ◽  
pp. 111-113
Author(s):  
Noriaki Shimada
Keyword(s):  
Wide Field ◽  
Myopic Traction Maculopathy

The new Myopic Traction Maculopathy Staging System

2020 ◽  
pp. 112067212093059 ◽  
Author(s):  
Barbara Parolini ◽  
Michele Palmieri ◽  
Alessandro Finzi ◽  
Gianluca Besozzi ◽  
Angela Lucente ◽  
...  
Keyword(s):  
Macular Hole ◽  
Phase 1 ◽  
Staging System ◽  
Wide Field ◽  
Phase 2 ◽  
Macular Detachment ◽  
Myopic Traction Maculopathy ◽  
Fundus Photographs ◽  
Two Phases ◽  
Stage 1

Purpose: To describe a comprehensive OCT-based classification of myopic traction maculopathy (MTM). Methods: Two hundred eighty-one eyes with MTM (visited from 2006 to 2018), were retrospectively reviewed for age, best-corrected-visual-acuity (BCVA), axial length (AL), optical coherence tomography (OCT), and wide-field color fundus-photographs. The study was divided in two Phases. Phase 1: MTM types were categorized with OCT and correlated with age and BCVA. The type of staphyloma was described. Phase 2: the evolution of MTM was studied evaluating at least three OCT exams of each eye taken at different timings (interval between each exam: 1–10 years). Results: Phase 1: We identified, four MTM retinal stages (1. Inner/Outer Maculoschisis; 2. Predominantly outer Maculoschisis; 3. Maculoschisis-Macular Detachment; 4. Macular Detachment) and three foveal stages (a. Normal fovea; b. Inner Lamellar-Macular-Hole; c. Full-Thickness-Macular-Hole). Outer-Lamellar-Macular-Holes and epiretinal abnormalities were associated findings. Stages 1 to 2 were younger than stages 3 to 4 ( p < 0.05). BCVA in stages 1, 2 was similar, and higher than stages 3, 4 ( p < 0.02). About 14% of eyes had no staphyloma, 73% of eyes had staphyloma type 1 or 2. MTM stages were not correlated with AL. Phase 2: The retina could change in time from stage 1 to 4, or the fovea could change from stage a to c. Mean evolution time from stage 1 to 2, stage 2 to 3, and 3 to 4 were 20, 12, 3 months, respectively. BCVA decreased over time as stages increased ( p = 0.47). Conclusion: The MSS Table displays a new classification, the natural evolution, and practical insights for the management of MTM.

scholarly journals Nobeyama 45 m mapping observations toward Orion A. II. Classification of cloud structures and variation of the 13CO/C18O abundance ratio due to far-UV radiation

2019 ◽  
Vol 71 (Supplement_1) ◽  
Author(s):  
Shun Ishii ◽  
Fumitaka Nakamura ◽  
Yoshito Shimajiri ◽  
Ryohei Kawabe ◽  
Takashi Tsukagoshi ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Column Density ◽  
Abundance Ratio ◽  
Data Cube ◽  
Wide Field ◽  
Dense Part ◽  
Dark Lane ◽  
The Individual ◽  
Far Ultraviolet

AbstractWe present results of the classification of cloud structures toward the Orion A Giant Molecular Cloud based on wide-field 12CO (J = 1–0), 13CO (J = 1–0), and C18O (J = 1–0) observations using the Nobeyama 45 m radio telescope. We identified 78 clouds toward Orion A by applying Spectral Clustering for Interstellar Molecular Emission Segmentation (SCIMES) to the data cube of the column density of 13CO. Well-known subregions such as OMC-1, OMC-2/3, OMC-4, OMC-5, NGC 1977, L1641-N, and the dark lane south filament (DLSF) are naturally identified as distinct structures in Orion A. These clouds can also be classified into three groups: the integral-shaped filament, the southern regions of Orion A, and the other filamentary structures in the outer parts of Orion A and the DLSF. These groups show differences in scaling relations between the physical properties of the clouds. We derived the abundance ratio between 13CO and C18O, $X_{^{13}\mathrm{CO}}/X_{\mathrm{C}^{18}\mathrm{O}}$, which ranges from 5.6 to 17.4 on median over the individual clouds. The significant variation of $X_{^{13}\mathrm{CO}}/X_{\mathrm{C}^{18}\mathrm{O}}$ is also seen within a cloud in both the spatial and velocity directions and the ratio tends to be high at the edge of the cloud. The values of $X_{^{13}\mathrm{CO}}/X_{\mathrm{C}^{18}\mathrm{O}}$ decrease from 17 to 10 with the median of the column densities of the clouds at the column density of $N_{\mathrm{C^{18}O}} \gtrsim 1 \times 10^{15}\:$cm−2 or visual extinction of AV ≳ 3 mag under the strong far-ultraviolet (FUV) environment of G0 &gt; 103, whereas it is almost independent of the column density in the weak FUV radiation field. These results are explained if the selective photodissociation of C18O is enhanced under a strong FUV environment and it is suppressed in the dense part of the clouds.

scholarly journals Computer-aided classification of suspicious pigmented lesions using wide-field images

2020 ◽  
Vol 195 ◽  
pp. 105631 ◽  
Author(s):  
Judith S. Birkenfeld ◽  
Jason M. Tucker-Schwartz ◽  
Luis R. Soenksen ◽  
José A. Avilés-Izquierdo ◽  
Berta Marti-Fuster
Keyword(s):  
Wide Field ◽  
Pigmented Lesions ◽  
Computer Aided

scholarly journals Classification of ASKAP VAST Radio Light Curves

2011 ◽  
Vol 7 (S285) ◽  
pp. 397-399 ◽  
Author(s):  
Umaa Rebbapragada ◽  
Kitty Lo ◽  
Kiri L. Wagstaff ◽  
Colorado Reed ◽  
Tara Murphy ◽  
...  
Keyword(s):  
Best Practices ◽  
Light Curve ◽  
Field Survey ◽  
Classification Performance ◽  
Light Curves ◽  
Classification Algorithms ◽  
Wide Field ◽  
Radio Transients ◽  
Instrument Sensitivity

AbstractThe VAST survey is a wide-field survey that observes with unprecedented instrument sensitivity (0.5 mJy or lower) and repeat cadence (a goal of 5 seconds) that will enable novel scientific discoveries related to known and unknown classes of radio transients and variables. Given the unprecedented observing characteristics of VAST, it is important to estimate source classification performance, and determine best practices prior to the launch of ASKAP's BETA in 2012. The goal of this study is to identify light-curve characterization and classification algorithms that are best suited for archival VAST light-curve classification. We perform our experiments on light-curve simulations of eight source types and achieve best-case performance of approximately 90% accuracy. We note that classification performance is most influenced by light-curve characterization rather than classifier algorithm.

scholarly journals Image recognition based on deep learning in Haemonchus contortus motility assays

2021 ◽  
Author(s):  
Martin Žofka ◽  
Linh Thuy Nguyen ◽  
Eva Mašátová ◽  
Petra Matoušková
Keyword(s):  
Drug Resistance ◽  
Deep Learning ◽  
Haemonchus Contortus ◽  
Model Organism ◽  
Absolute Error ◽  
Wide Field ◽  
Forecast Precision ◽  
Wide Field Of View ◽  
Poor Efficacy

Poor efficacy of some anthelmintics and rising concerns about the widespread drug resistance have highlighted the need for new drug discovery. The parasitic nematode Haemonchus contortus is an important model organism widely used for studies of drug resistance and drug screening with the current gold standard being the motility assay. We applied a deep learning approach Mask R-CNN for analysing motility videos and compared it to other commonly used algorithms with different levels of complexity, namely Wiggle Index and Wide Field-of-View Nematode Tracking Platform. Mask R-CNN consistently outperformed the other algorithms in terms of the forecast precision across the videos containing varying rates of motile worms with a mean absolute error of 5.6%. Using Mask R-CNN for motility assays confirmed the common problem of algorithms that use Non-Maximum Suppression in detecting overlapping objects, which negatively impacted the overall precision. The use of intersect over union (IoU) as a measure of the classification of motile / non-motile instances had an overall accuracy of 89%. In comparison to the existing methods evaluated here, Mask R-CNN performed better and we can anticipate that this method will broaden the number of possible approaches to video analysis of worm motility. IoU has shown promise as a good metric for evaluating motility of individual worms.

scholarly journals Classification of Planetary Nebulae through Deep Transfer Learning

Galaxies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 88
Author(s):  
Dayang N. F. Awang Iskandar ◽  
Albert A. Zijlstra ◽  
Iain McDonald ◽  
Rosni Abdullah ◽  
Gary A. Fuller ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Planetary Nebulae ◽  
Parameter Tuning ◽  
Wide Field ◽  
Morphological Classification ◽  
Trade Offs ◽  
Research Platform ◽  
Astronomical Images ◽  
Future Work

This study investigate the effectiveness of using Deep Learning (DL) for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted the deep transfer learning approach using three ImageNet pre-trained algorithms. This study was conducted using images from the Hong Kong/Australian Astronomical Observatory/Strasbourg Observatory H-alpha Planetary Nebula research platform database (HASH DB) and the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). We found that the algorithm has high success in distinguishing True PNe from other types of objects even without any parameter tuning. The Matthews correlation coefficient is 0.9. Our analysis shows that DenseNet201 is the most effective DL algorithm. For the morphological classification, we found for three classes, Bipolar, Elliptical and Round, half of objects are correctly classified. Further improvement may require more data and/or training. We discuss the trade-offs and potential avenues for future work and conclude that deep transfer learning can be utilized to classify wide-field astronomical images.

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