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Automated red blood cells extraction from holographic images using fully convolutional neural networks

Automated red blood cells extraction from holographic images using fully convolutional neural networks
Yi, FaliuMoon, In KyuJavidi, Bahram
DGIST Authors
Moon, In Kyu
Issue Date
Biomedical Optics Express, 8(10), 4466-4479
Article Type
3-Dimensional IdentificationAlgorithmsBioinformaticsBloodCellsContrastConvolutionConvolutional Neural NetworkDigital Holographic MicroscopyDigital HolographyExtractionHologramsHolographyImage ProcessingImage SegmentationMachine VisionMarker-Controlled Watershed SegmentationMarker-Controlled WatershedsMedical And Biological ImagingMedical ImagingMicroscopic ExaminationNeural NetworksPhase MicroscopyRecognitionSegmentationSegmentation AccuracyThree-Dimensional Image ProcessingTrackingVisualization
In this paper, we present two models for automatically extracting red blood cells (RBCs) from RBCs holographic images based on a deep learning fully convolutional neural network (FCN) algorithm. The first model, called FCN-1, only uses the FCN algorithm to carry out RBCs prediction, whereas the second model, called FCN-2, combines the FCN approach with the marker-controlled watershed transform segmentation scheme to achieve RBCs extraction. Both models achieve good segmentation accuracy. In addition, the second model has much better performance in terms of cell separation than traditional segmentation methods. In the proposed methods, the RBCs phase images are first numerically reconstructed from RBCs holograms recorded with off-axis digital holographic microscopy. Then, some RBCs phase images are manually segmented and used as training data to fine-tune the FCN. Finally, each pixel in new input RBCs phase images is predicted into either foreground or background using the trained FCN models. The RBCs prediction result from the first model is the final segmentation result, whereas the result from the second model is used as the internal markers of the marker-controlled transform algorithm for further segmentation. Experimental results show that the given schemes can automatically extract RBCs from RBCs phase images and much better RBCs separation results are obtained when the FCN technique is combined with the marker-controlled watershed segmentation algorithm. © 2017 Optical Society of America.
OSA - The Optical Society
Related Researcher
  • Author Moon, Inkyu Intelligent Imaging and Vision Systems Laboratory
  • Research Interests
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Department of Robotics EngineeringIntelligent Imaging and Vision Systems Laboratory1. Journal Articles

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