Cai Sijing, Tian Yunxian, Lui Harvey, Zeng Haishan, Wu Yi, Chen Guannan
Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China.
School of Information Science and Engineering, Fujian University of Technology, Fuzhou, China.
Quant Imaging Med Surg. 2020 Jun;10(6):1275-1285. doi: 10.21037/qims-19-1090.
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.
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 was explored. A set of MPM 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.
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.
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 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 images with low resolution. This implementation supplies an automatic segmentation model based on deep learning for high-precision segmentation of MPM images .
多光子显微镜（MPM）为临床医学活检提供了一种可行的方法，但由于缺乏有效的图像处理方法，尤其是自动分割技术，其尚未应用于临床。分割技术仍然是MPM成像技术最具挑战性的任务之一。
基于深度学习的MPM成像分割模型是解决这一问题的最有效方法之一。本文探讨了使用卷积神经网络（CNN）模型分割皮肤细胞MPM图像的实用性。在Python环境下使用TensorFlow成功分割了一组分辨率为128×128的MPM皮肤细胞图像。提出了一种名为Dense-UNet的新型深度学习分割模型。Dense-UNet基于U-net结构，采用密集连接加深网络架构深度并实现特征重用。该模型包括四个扩展模块（每个模块由四个下采样层组成）以提取特征。
使用运行在735nm的飞秒钛宝石激光器从背侧前臂采集了60张训练图像。图像分辨率为128×128像素。实验结果证实，Dense-UNet的准确率（92.54%）高于U-Net（88.59%），损失值显著更低，为0.1681。Dense-UNet的骰子系数值为90.60%，比U-Net高出11.07%。Dense-UNet、U-Net和Seg-Net的F1分数分别为93.35%、90.02%和85.04%。
加深的下采样路径提高了模型捕捉细胞精细边界特征的能力，而对称的上采样路径根据测试结果提供了更准确的定位。这些结果首次通过引入深度CNN弥合Dense-UNet技术中的这一差距来实现MPM图像的分割。Dense-UNet在MPM图像方面达到了超现代性能，尤其是对于低分辨率图像。该实现为MPM图像的高精度分割提供了一种基于深度学习的自动分割模型。
