Oktay Ayse Betul, Gurses Anıl
Department of Computer Engineering, Istanbul Medeniyet University, Istanbul, Turkey.
Electrical and Electronics Engineering Department, Istanbul Medeniyet University, Istanbul, Turkey.
Micron. 2019 May;120:113-119. doi: 10.1016/j.micron.2019.02.009. Epub 2019 Feb 22.
With the growing amount of high resolution microscopy images automatic nano-particle detection, shape analysis and size determination have gained importance for providing quantitative support that gives important information for the evaluation of the material. In this paper, we present a new method for detection of nano-particles and determination of their shapes and sizes simultaneously with deep learning. The proposed method employs multiple output convolutional neural networks (MO-CNN) and has two outputs: first is the detection output that gives the locations of the particles and the other one is the segmentation output for providing the boundaries of the nano-particles. The final sizes of particles are determined with the modified Hough algorithm that runs on the segmentation output. The proposed method is tested and evaluated on a dataset containing 17 TEM images of FeO and silica coated nano-particles. Also, we compared these results with U-net algorithm which is a popular deep learning method. The experiments showed that the proposed method has 98.23% accuracy for detection and 96.59% accuracy for segmentation of nano-particles.
随着高分辨率显微镜图像数量的不断增加，自动纳米颗粒检测、形状分析和尺寸测定对于提供定量支持变得至关重要，这些支持为材料评估提供重要信息。在本文中，我们提出了一种利用深度学习同时检测纳米颗粒并确定其形状和尺寸的新方法。所提出的方法采用多输出卷积神经网络（MO-CNN），有两个输出：第一个是检测输出，给出颗粒的位置，另一个是分割输出，用于提供纳米颗粒的边界。颗粒的最终尺寸通过在分割输出上运行的改进霍夫算法确定。所提出的方法在包含17张FeO和二氧化硅包覆纳米颗粒的透射电子显微镜（TEM）图像的数据集上进行了测试和评估。此外，我们将这些结果与一种流行的深度学习方法U-net算法进行了比较。实验表明，所提出的方法对纳米颗粒检测的准确率为98.23%，分割的准确率为96.59%。
