Abstract
In this paper, we consider the task of detecting platelets in images of diluted whole blood taken with a lens-free microscope. Despite having several advantages over traditional microscopes, lens-free imaging systems have the significant challenge that the resolution of the system is typically limited by the pixel dimensions of the image sensor. As a result of this limited resolution, detecting platelets is very difficult even by manual inspection of the images due to the fact that platelets occupy just a few pixels of the reconstructed image. To address this challenge, we develop an optical model of diluted whole blood to generate physically realistic simulated holograms suitable for training machine learning models in a supervised manner. We then use this model to train a convolutional neural network (CNN) for platelet detection and validate our approach by developing a novel optical configuration which allows collecting both lens-free and fluorescent microscopy images of the same field of view of diluted whole blood samples with fluorescently labeled platelets.
Highlights
Lens-free imaging (LFI) is a form of digital microscopic holography which records the diffraction patterns of a specimen illuminated with coherent light and reconstructs an image of the specimen by inverting a mathematical model of the light diffraction process
This model is consistent with scattering measurements taken of red blood cells (RBCs) which have noted that at the wavelength of light used by our LFI system (637nm) RBCs do not absorb light [20]
We developed a tandem microscopy setup which allows for both fluorescent and LFI images with a partially overlapping field of view (FOV) to be recorded within a few seconds of each other
Summary
Lens-free imaging (LFI) is a form of digital microscopic holography which records the diffraction patterns ( referred to as holograms) of a specimen illuminated with coherent light (e.g., from a laser) and reconstructs an image of the specimen by inverting a mathematical model of the light diffraction process. It is often very challenging to accurately locate and identify platelets even by manual inspection in reconstructed lens-free images due to 1) their small size relative to the resolution of the image 2) the relatively small signal that they generate relative to the other cells in the image (predominately red blood cells, which have roughly an order of magnitude larger volume), and 3) the fact that there is roughly an order of magnitude fewer platelets compared to red blood cells in a typical sample Combined, these issues significantly limit the potential of constructing even moderately sized training and testing datasets, which precludes the use of large-scale supervised methods like neural networks but any supervised learning method (such as training a simple classifier based on pre-defined image features) as well as making the quantitative evaluation of any object-detection approach very challenging. This paper extends a preliminary conference publication [19] by adding additional description of the optical setup and image analysis pipeline used for the verification protocol as well as testing the method on a larger set of images
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