Background
Diabetic retinopathy (DR), the most common cause of vision loss, is caused by damage to the small blood vessels in the retina. If untreated, it may result in varying degrees of vision loss and even blindness. Since DR is a silent disease that may cause no symptoms or only mild vision problems, annual eye exams are crucial for early detection to improve chances of effective treatment where fundus cameras are used to capture retinal image. However, fundus cameras are too big and heavy to be transported easily and too costly to be purchased by every health clinic, so fundus cameras are an inconvenient tool for widespread screening. Recent technological developments have enabled to use of smartphones in designing small-sized, low-power, and affordable retinal imaging systems to perform DR screening and automated DR detection using image processing methods. In this paper, we investigate the smartphone-based portable retinal imaging systems available on the market and compare their image quality and the automatic DR detection accuracy using a deep learning framework.
Results
Based on the results, iNview retinal imaging system has the largest field of view and better image quality compared with iExaminer, D-Eye, and Peek Retina systems. The overall classification accuracy of smartphone-based systems are sorted as 61%, 62%, 69%, and 75% for iExaminer, D-Eye, Peek Retina, and iNview images, respectively. We observed that the network DR detection performance decreases as the field of view of the smartphone-based retinal systems get smaller where iNview is the largest and iExaminer is the smallest.
Conclusions
The smartphone-based retina imaging systems can be used as an alternative to the direct ophthalmoscope. However, the field of view of the smartphone-based retina imaging systems plays an important role in determining the automatic DR detection accuracy.
BackgroundDuring brain development, neurons migrate from germinal zones to their final positions to assemble neural circuits. A unique saltatory cadence involving cyclical organelle movement (e.g., centrosome motility) and leading-process actomyosin enrichment prior to nucleokinesis organizes neuronal migration. While functional evidence suggests that leading-process actomyosin is essential for centrosome motility, the role of the actin-enriched leading process in globally organizing organelle transport or traction forces remains unexplored.ResultsWe show that myosin ii motors and F-actin dynamics are required for Golgi apparatus positioning before nucleokinesis in cerebellar granule neurons (CGNs) migrating along glial fibers. Moreover, we show that primary cilia are motile organelles, localized to the leading-process F-actin-rich domain and immobilized by pharmacological inhibition of myosin ii and F-actin dynamics. Finally, leading process adhesion dynamics are dependent on myosin ii and F-actin.ConclusionsWe propose that actomyosin coordinates the overall polarity of migrating CGNs by controlling asymmetric organelle positioning and cell-cell contacts as these cells move along their glial guides.Electronic supplementary materialThe online version of this article (doi:10.1186/1749-8104-9-26) contains supplementary material, which is available to authorized users.
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