Development of a machine learning platform for analysis of mitochondrial features in live-cell images
Tarkan, Yalçın, author.

Development of a machine learning platform for analysis of mitochondrial features in live-cell images

Tarkan, Yalçın, author.

Yazar Ek Girişi
Tarkan, Yalçın, author.

Fiziksel Tanımlama
vii, 53 leaves: charts;+ 1 computer laser optical disc.

It is a laborious and error-prone manual process to mark the organelles in 2D and 3D images of living cells and identify the behavioral feedback to stimulations under measured conditions. This manual process can be simplified by being largely automated with machine learning techniques. We created a machine learning-based software platform named MitoML, which extracts sub-cellular structures, specifically mitochondria, and helps to identify the effects of external factors or changes under natural conditions. We investigate appropriate machine learning techniques for these objectives. Image processing and segmentation techniques with neural networks, enable researchers to carry out experiments with much better accuracy and a larger scale by automatically segmenting and counting the mitochondria, calculate the energy potentials based on region brightness. This way, analysis of mitochondria feedback in healthy and cancer cells under various conditions, such as nanomedicine and different treatment therapies, can be performed using MitoML. As a result of our work, we proposed a cascaded neural network architecture that can identify and count mitochondria, quantify its energy levels in fluorescence and other microscopy images, fast and at a standard reliable accuracy. Our test results outperformed the classical image processing algorithms provided in segmentation tools and software for medical image segmentation which was taken as a base line. Achieved accuracy rates 93.4% and %89.6 according to Dice and IoU metrics respectively are also better than any other related work encountered during the research. The proposed method can be improved to cover other sub-cellular structures relieving the researchers from non-standardized and laborious manual work which is prone to human error.

Konu Başlığı
Machine learning
Neural networks (Computer science)
Artificial Intelligence.
Image processing
Mitochondrial DNA

Yazar Ek Girişi
Tuğlular, Tuğkan,
Özçelik, Serdar,

Tüzel Kişi Ek Girişi
İzmir Institute of Technology. Computer Engineering.

Tek Biçim Eser Adı
Thesis (Master)--İzmir Institute of Technology:Computer Engineering.
İzmir Institute of Technology: Computer Engineering--Thesis (Master).

Elektronik Erişim
Access to Electronic Versiyon.

KütüphaneMateryal TürüDemirbaş NumarasıYer NumarasıDurumu/İade Tarihi
IYTETezT002659QA76.87 .T18 2022Tez Koleksiyonu