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Automatic detection, localization and segmentation of nano-particles with deep learning in microscopy images
تشخیص خودکار ، بومی سازی و تقسیم نانو ذرات با یادگیری عمیق در تصاویر میکروسکوپی-2019 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 nanoparticles
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. Keywords: Nano-particle | Deep learning | Object detection | MO-CNN | Hough transform |
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