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

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.

[1]  Hai Su,et al.  Deep Learning in Microscopy Image Analysis: A Survey , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Ayse Betül Oktay,et al.  Segmentation of Fe3O4 nano particles in TEM images , 2014, 2014 22nd Signal Processing and Communications Applications Conference (SIU).

[3]  Taeghwan Hyeon,et al.  Ultra-large-scale syntheses of monodisperse nanocrystals , 2004, Nature materials.

[4]  Tsuyoshi Kato,et al.  Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images , 2018, Food and Environmental Virology.

[5]  Andrew Zisserman,et al.  Learning to Detect Partially Overlapping Instances , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Tian Xia,et al.  DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM , 2016, Journal of structural biology.

[7]  Guangwen Yang,et al.  A fast method for particle picking in cryo-electron micrographs based on fast R-CNN , 2017 .

[8]  Ayse Betül Oktay,et al.  Nanoparticle detection from TEM images with deep learning , 2018, 2018 26th Signal Processing and Communications Applications Conference (SIU).

[9]  Chihway Chang,et al.  Radio frequency interference mitigation using deep convolutional neural networks , 2016, Astron. Comput..

[10]  R. Glaeser,et al.  Review: automatic particle detection in electron microscopy. , 2001, Journal of structural biology.

[11]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[12]  Xin Zheng,et al.  Fast and robust segmentation of white blood cell images by self-supervised learning. , 2018, Micron.

[13]  Tao Ma,et al.  Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform. , 2018, Micron.

[14]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[15]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[16]  Yanan Zhu,et al.  A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy , 2016, BMC Bioinformatics.

[17]  Alexis Rohou,et al.  cisTEM: User-friendly software for single-particle image processing , 2017, bioRxiv.

[18]  Bani K. Mallick,et al.  BAYESIAN OBJECT CLASSIFICATION OF GOLD NANOPARTICLES , 2013, 1312.1560.

[19]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Kevin W. Eliceiri,et al.  ImageJ2: ImageJ for the next generation of scientific image data , 2017, BMC Bioinformatics.