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High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks

High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks
Yi, FaliuPark, SeonghwanMoon, Inkyu
DGIST Authors
Yi, FaliuPark, SeonghwanMoon, Inkyu
Issued Date
Author Keywords
cell countingdeep learningdigital holographic microscopyholography applicationoptical information processingred blood cell analysis
SIGNIFICANCE: Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell level in biomedical images for biomarker discovery and disease diagnostics. However, the biological cell analysis based on phase information of images is inefficient due to the complexity of numerical phase reconstruction algorithm applied to raw hologram images. New cell study methods based on diffraction pattern directly are desirable. AIM: Deep fully convolutional networks (FCNs) were developed on raw hologram images directly for high-throughput label-free cell detection and counting to assist the biological cell analysis in the future. APPROACH: The raw diffraction patterns of RBCs were recorded by use of DHM. Ground-truth mask images were labeled based on phase images reconstructed from RBC holograms using numerical reconstruction algorithm. A deep FCN, which is UNet, was trained on the diffraction pattern images to achieve the label-free cell detection and counting. RESULTS: The implemented deep FCNs provide a promising way to high-throughput and label-free counting of RBCs with a counting accuracy of 99% at a throughput rate of greater than 288 cells per second and 200  μm  ×  200  μm field of view at the single cell level. Compared to convolutional neural networks, the FCNs can get much better results in terms of accuracy and throughput rate. CONCLUSIONS: High-throughput label-free cell detection and counting were successfully achieved from diffraction patterns with deep FCNs. It is a promising approach for biological specimen analysis based on raw hologram directly.
Related Researcher
  • 문인규 Moon, Inkyu 로봇및기계전자공학과
  • Research Interests 지능형 영상시스템; AI기반 영상분석; AI기반 암호시스템; Intelligent Imaging Systems; AI-based Image Analysis; AI-based Cryptosystems & Cryptanalysis
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Department of Robotics and Mechatronics Engineering Intelligent Imaging and Vision Systems Laboratory 1. Journal Articles


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