Automatic detection, localization and segmentation of nano-particles with deep learning in microscopy images

Oktay A. B., Gurses A.

MICRON, vol.120, pp.113-119, 2019 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 120
  • Publication Date: 2019
  • Doi Number: 10.1016/j.micron.2019.02.009
  • Journal Name: MICRON
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.113-119
  • Keywords: Nano-particle, Deep learning, Object detection, MO-CNN, Hough transform
  • Yıldız Technical University Affiliated: No


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 Fe3O4 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.