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

Faliu Yi,Bahram Javidi,Inkyu Moon

doi:10.1364/boe.8.004466

Abstract

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.

Highlights

Because the limitations inherent in traditional two-dimensional (2D) imaging techniques when used with transparent or semitransparent biological organisms, three-dimensional (3D) imaging systems have been developed and are widely used for transparent or semitransparent biological specimen imaging and visualization [1,2,3,4,5,6]
Experimental results All the red blood cells (RBCs) analyzed in our experiment were taken from healthy laboratory personnel in the Laboratoire Suisse d’ Analyse Du Dopage, CHUV and their holograms recorded with off-axis digital holographic microscopy (DHM)
Two models based on fully convolutional neural network (FCN) were developed and used for automated RBCs extraction in RBCs phase images numerically reconstructed from digital holograms obtained using off-axis DHM

Summary

Introduction

Because the limitations inherent in traditional two-dimensional (2D) imaging techniques when used with transparent or semitransparent biological organisms, three-dimensional (3D) imaging systems have been developed and are widely used for transparent or semitransparent biological specimen imaging and visualization [1,2,3,4,5,6]. We combined the marker-controlled watershed algorithm with morphological operations and segmented RBCs phase images obtained using the DHM technique with good results [26]. In the second scheme named as FCN-2, we combine the FCN model with the marker-controlled watershed transform algorithm to segment the RBCs. In FCN-2, we only use the fully convolutional neural network to predict the inner part of each red blood cell and regard the predicted results as internal markers of marker-controlled watershed algorithm so as to further segment the RBCs. In the second scheme, the training label image is not the mask of all the segmented cells; it is erosion results of that mask, which represents the inner area of each RBC. We first use a 3D imaging technique called off-axis DHM to record these RBCs and apply the numerical reconstruction algorithm to reconstruct RBCs phase images from their holograms.

Off-axis digital holographic microscopy

Fully convolutional neural networks

RBCs segmentation

Conclusions