Convolutional neural networks (CNNs) are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained CNN model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union of ~86%. Various morphological and dynamical cardiac parameters can be quantified accurately with automatically segmented heart regions. This study demonstrates an efficient heart segmentation method to analyze OCM images of the beating heart in Drosophila.

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During quality-assurance procedures in the mass production of small-sized curved optical lenses, fine defects are usually detected via manual observation, which is not recommended owing to the associated drawbacks of high error rate, low efficiency, and nonamenability to quantitative analysis. To address this concern, this paper presents a comprehensive defect-detection system based on transmitted fringe deflectometry, dark-field illumination, and light transmission. Experimental results obtained in this study reveal that the proposed method demonstrates efficient and accurate detection of several microdefects occurring in small-sized optical lenses, thereby providing valuable insights into the optimization of parameters concerning the mass production of optical lenses. The proposed system can be applied to the actual mass production of small-sized curved optical lenses.