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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, Faliu; Moon, In Kyu; Javidi, Bahram
- DGIST Authors
- Moon, In Kyu
- Issue Date
- Biomedical Optics Express, 8(10), 4466-4479
- Article Type
- 3-Dimensional Identification; Algorithms; Bioinformatics; Blood; Cells; Contrast; Convolution; Convolutional Neural Network; Digital Holographic Microscopy; Digital Holography; Extraction; Holograms; Holography; Image Processing; Image Segmentation; Machine Vision; Marker-Controlled Watershed Segmentation; Marker-Controlled Watersheds; Medical And Biological Imaging; Medical Imaging; Microscopic Examination; Neural Networks; Phase Microscopy; Recognition; Segmentation; Segmentation Accuracy; Three-Dimensional Image Processing; Tracking; Visualization
- 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
Moon, In Kyu
Intelligent Imaging and Vision Systems Laboratory
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- Department of Robotics EngineeringIntelligent Imaging and Vision Systems Laboratory1. Journal Articles
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