Convolutional Neural Networks for Cellular Segmentation
Name
Sten-Oliver Salumaa
Abstract
There is a persistent demand for work-assisting algorithms in industry. Using present-day technology, it is possible to free people from mundane tasks so they can concentrate on work that requires human skills and flexibility. Deep learning methods can complete tasks that were previously considered hard or even impossible for machines.
One example of this kind of task is segmenting brightfield microscopy images of cells. This work is needed mostly in biomedical laboratories and pharmaceutical companies that must analyse and quantify vast amounts of image data. Current workflows avoid useful brightfield imagery because automatic industrial solutions for segmentation do not exist. Manual annotation is very challenging and time consuming, even for experienced professionals.
The goal of the thesis is to demonstrate that deep learning can solve the task of segmenting challenging brightfield images. The developed solution opens new experimental approaches, saving time and resources for biomedical scientists across the globe.
One example of this kind of task is segmenting brightfield microscopy images of cells. This work is needed mostly in biomedical laboratories and pharmaceutical companies that must analyse and quantify vast amounts of image data. Current workflows avoid useful brightfield imagery because automatic industrial solutions for segmentation do not exist. Manual annotation is very challenging and time consuming, even for experienced professionals.
The goal of the thesis is to demonstrate that deep learning can solve the task of segmenting challenging brightfield images. The developed solution opens new experimental approaches, saving time and resources for biomedical scientists across the globe.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Leopold Parts, Dmytro Fishman
Defence year
2018