Deep learning boosts the performance of automatic OCT segmentation, which is a prerequisite for standardized diagnostic and therapeutic procedures. However, training deep neural network requires laborious data labeling, and the trained models only work well on data from the same manufacturer, imaging protocol, and region of interest. Here we propose a novel learning method to reduce labeling costs. By labeling and training on a single image, we achieved segmentation accuracy comparable to that of a U-Net model trained on ~25 to 50 labeled images. This reduction in labeling costs could significantly improve the flexibility and generalization of deep-learning-based OCT segmentation.
By taking advantage of the inherent flexibility of low-cost 3D printing materials, we achieved an optical focus tuning accuracy of ~5 micron with a novel structure design. It shrinks the mechanical displacement by a factor of ~11 through a seesaw-like component. Combing with the built-in flashlight illumination and an off-the-shelf smartphone lens, the total manufacturing cost of our smartphone-based microscope is less than 4 USD. We demonstrated the capability of this design in imaging thick biological specimens. We further applied this device in the cell culture monitoring of VX2 tumor cells because of its portability and flexibility.
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