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Background: Multiphoton microscopy (MPM) offers a feasible approach for the biopsy in clinical medicine, but it has not been used in clinical applications due to the lack of efficient image processing methods, especially the automatic segmentation technology. Segmentation technology is still one of the most challenging assignments of the MPM imaging technique. Methods:The MPM imaging segmentation model based on deep learning is one of the most effective methods to address this problem. In this paper, the practicability of using a convolutional neural network (CNN) model to segment the MPM image of skin cells in vivo was explored. A set of MPM in vivo skin cells images with a resolution of 128×128 was successfully segmented under the Python environment with TensorFlow. A novel deep-learning segmentation model named Dense-UNet was proposed. The Dense-UNet, which is based on U-net structure, employed the dense concatenation to deepen the depth of the network architecture and achieve feature reuse. This model included four expansion modules (each module consisted of four down-sampling layers) to extract features.Results: Sixty training images were taken from the dorsal forearm using a femtosecond Ti:Sa laser running at 735 nm. The resolution of the images is 128×128 pixels. Experimental results confirmed that the accuracy of Dense-UNet (92.54%) was higher than that of U-Net (88.59%), with a significantly lower loss value of 0.1681. The 90.60% Dice coefficient value of Dense-UNet outperformed U-Net by 11.07%. The F1-Score of Dense-UNet, U-Net, and Seg-Net was 93.35%, 90.02%, and 85.04%, respectively. Conclusions:The deepened down-sampling path improved the ability of the model to capture cellular fined-detailed boundary features, while the symmetrical up-sampling path provided a more accurate location based on the test result. These results were the first time that the segmentation of MPM in vivo images had been adopted by introducing a deep CNN to bridge this gap in Dense-UNet technology. Dense-UNet has reached ultramodern performance for MPM images, especially for in vivo images with low resolution. This implementation supplies an automatic segmentation model based on deep learning for high-precision segmentation of MPM images in vivo.

Pesticides, extensively used in agriculture production, have received enormous attention because of their potential threats to the environment and human health. Hence, in this study, a kind of highly sensitive and stable hybrid surface-enhanced Raman scattering (SERS)-active substrates constructed with flower-like two-dimensional molybdenum sulfide and Ag (MoS2@Ag) has been developed, and then the above substrate was sequentially utilized in the recyclable detection of pesticide residues on several kinds of fruits and vegetables. In the first place, the excellent photocatalytic performance of the MoS2@Ag hybrid substrate was demonstrated, which was attributed to the inhibition of electron–hole combination after the formation of Schottky barrier between the Ag NPs and MoS2 matrix. Thereafter, two calibration curves with ultra-low limits of detection (LOD) as 6.4 × 10–7 and 9.8 × 10–7 mg/mL were established for the standard solutions of thiram (tetramethylthiuram disulfide, TMTD) and methyl parathion (MP), and then the recyclable assay of their single and mixed residues on eggplant, Chinese cabbage, grape, and strawberry was successfully realized. It is interesting to note that the detection recoveries from 95.5 to 63.1% for TMTD and 92.3 to 62.6% for MP are greatly dependent on the size and surface roughness of these foods. In a word, the MoS2@Ag composite matrix shows attractive SERS and photocatalysis performance, and it is expected to have the potential application on food safety monitoring.