Automatic Detection of Microorganisms on Microscopic Images of Fluid Samples using Machine Learning

Type: MA thesis

Status: running

Date: March 15, 2022 - September 15, 2022

Supervisors: Frauke Wilm, Andreas Maier, Dr. Lucien Weiss (Polytechnique Montréal)

The objective of this thesis is to apply machine learning tools to rapidly analyze large datasets of microscopic images to identify and classify microbial infections.
Microorganisms can cause a wide range of diseases, e.g. tuberculosis, and left untreated, infections can quickly become fatal [1]. Fortunately, the discovery of penicillin and the subsequent invention of other antibiotics has significantly decreased the lethality of these diseases. This early success has led to an era of frequent use of antibiotics [2]. However, overprescription of these drugs has caused bacteria to develop mechanisms that confer resistance against particular drugs. This emergence of multi-drug resistance strains poses an extreme risk [2]. Therefore, these developments necessitate a more targeted application of antibiotics, which, however, requires the classification and characterization of bacteria found in patient samples. Existing methods for classifying microorganisms can be categorized into chemical, physical, molecular biological, and morphological methods [1]. While the latter is positioned to be the most direct and cost-effective method of the four, it requires a high amount of manual work and is thereby laborious and time-consuming [1].
The Weiss group develops and applies technologies for rapidly imaging and analyzing biological samples using high-resolution fluorescence microscopy, microfluidics. In collaboration with the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg, this toolkit is extended by computer vision components to analyze the image data.
Computer vision tools are well-suited for image classification problems and have been widely applied to microscopy. On relatively pure laboratory samples these tools can perform astonishingly well [1]. However, a single droplet of a fluid from a patient sample is significantly more challenging as it can contain a large array of different types of objects in very large quantities which complicates the detection and classification of single objects. Under the assumption that high quality and high-resolution microscopy images are provided, the key challenges are thus twofold: first to find the objects of interest and second to classify them.
In general, various approaches to solving this problem can be considered:
– Pursuing a supervised approach by establishing a large, bounding box or segment annotated database to train a deep neural net that can process unseen data and extract location and class of objects.
– Following semi-supervised techniques by only establishing a small, annotated database to train the classifier and using uniform or random segments of the input data.
– Processing uniform or random segments unsupervised by clustering them in multiple distinct classes.
In this thesis, we will investigate these strategies to address the problem of automatic object detection in microscopic images of fluid samples.
The thesis comprises the following tasks:
– literature review concerning sources and state-of-the-art approaches to construct classifiers with limited data available
– Implementation of one or more solutions to address the classification problem
– Evaluation of proposed method and emerging challenges
– Documentation and presentation of the findings, documentation of code
– Discussion of progress in weekly meetings with mentors Dr. Lucien Weiss and Frauke Wilm
[1] Zhang, Jinghua, Chen Li, and Marcin Grzegorzek. “Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer.” arXiv preprint arXiv:2108.00358 (2021).
[2] Casadevall, Arturo. “Crisis in infectious diseases: time for a new paradigm?.” Clinical infectious diseases 23.4 (1996): 790-794.