Active contour and deep learning methods for single-cell segmentation in microscopy images

This work introduces methods for single-cell segmentation of microscopy images. The developed methods are based on active contours and deep learning. In the first thesis point, a reinitialization method is developed for level sets that is based on the phase field theory. When the phase field functional is minimized, it forms a smooth transition between the two phases. This phase transition can be used as a reinitialization technique when combined with level sets. However, we show that the original phase field functional moves the zero transition away from its original position as an undesired side effect. We propose a proper combination of the original gradient based and a second order term to eliminate this effect and shown to be effective when combined with different active contour models. In the second thesis point, we propose an selective active contour model in 3D for the instance segmentation of 3D microscopy images. The proposed method uses surface and volume priors and a shape prior that is a combination of the former two to describe common shapes of biological objects. The resulting segmentation method is then embedded into a popular 3D medical image analysis software for semi-automatic segmentation shown to greatly reduce the annotation time. The third thesis point investigates the applications of the image-to-image translation method for automatic data augmentation. The first proposed method synthesizes artificial instance masks using a traditional parametric cell population simulation tool and then applies the learned image-to-image translation model to synthesize the corresponding microscopy images. The second method tries to learn the discrete instance masks directly from the data using generative adversarial networks thus enabling the synthesis of complex tissue structures. The proposed methods shown to increase the test accuracy when used with different instance segmentation models

Novel O-linked Sialoglycan Structures in Human Urinary Glycoproteins

Glycopeptides represent cross-linked structures between chemically and physically different biomolecules. Mass spectrometric analysis of O-glycopeptides may reveal the identity of the peptide, the composition of the glycan and even the connection between certain sugar units, but usually only the combination of different MS/MS techniques provides sufficient information for reliable assignment. Currently, HCD analysis followed by diagnostic sugar fragment-triggered ETD or EThcD experiments is the most promising data acquisition protocol. However, the information content of the different MS/MS data is handled separately by search engines. We are convinced that these data should be used in concert, as we demonstrate in the present study. First, glycopeptides bearing the most common glycans can be identified from EThcD and/or HCD data. Then, searching for Y-0 (the gas-phase deglycosylated peptide) in HCD spectra, the potential glycoforms of these glycopeptides could be lined up. Finally, these spectra and the corresponding EThcD data can be used to verify or discard the tentative assignments and to obtain further structural information about the glycans. We present 18 novel human urinary sialoglycan structures deciphered using this approach. To accomplish this in an automated fashion further software development is necessary