A deep learning computer vision system for image based cytometry
Date
2017-02-02T18:55:31Z
Authors
Lisicki, Michal
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Abstract
A Complete Blood Count (CBC) is one of the most commonly deployed tests for
checking the overall medical condition of a human body. It results in a summary of
blood cell related measures allowing for a quick preliminary detection of common
diseases. Image based cytometry is a modern way of overcoming common efficiency
issues related to current gold standard method of Coulter Counter analysis.
This thesis describes a computer vision pipeline for measuring main components of
CBC based on methods with varied level of human influence in feature design. The low
quality input images are acquired from a prototype lensless microscope. Rule-based
approaches proved sufficient in cases of red blood cells and platelets analysis and their
configuration is explored in more details at the segmentation stage.
Leukocyte differentiation is a subproblem of CBC requiring more sophisticated
classification approach. The comparison of results based on rule-based, classical
and deep learning methods is presented. Support Vector Machines and k-Nearest
Neighbours were chosen as representatives of classical approaches. The deep learning
scheme has the advantage of finding quick solutions to complex problems involving
highly dimensional and hierarchical structure of underlying solutions. It also alleviates
the amount of work needed by human experts to design appropriate descriptors.
The results discussed here include promising enumeration and feature description
of the objects of interest. Their linear dependence with gold standard clinical results
proved their usability for point of care blood analysis. Classification results showed
superiority of convolutional neural networks in several important aspects. Further
analysis explored the learned parameters and analyzed wrongly classified cases in order
to understand better what kind of cytological objects are most fragile for particular
classifier and why.
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Keywords
computer vision, cytometry, deep learning, cell analysis, convolutional neural network