Learning Nanoscale Motion Patterns of Vesicles in Living Cells

Detecting and analyzing nanoscale motion patterns of vesicles, smaller than the microscope resolution (~250 nm), inside living biological cells is a challenging problem. State-of-the-art CV approaches based on detection, tracking, optical flow or deep learning perform poorly for this problem. We propose an integrative approach, built upon physics based simulations, nanoscopy algorithms, and shallow residual attention network to make it possible for the first time to analysis sub-resolution motion patterns in vesicles that may also be of sub-resolution diameter. Our results show state-of-the-art performance, 89% validation accuracy on simulated dataset and 82% testing accuracy on an experimental dataset of living heart muscle cells imaged under three different pathological conditions. We demonstrate automated analysis of the motion states and changed in them for over 9000 vesicles. Such analysis will enable large scale biological studies of vesicle transport and interaction in living cells in the future

A method for astral microtubule tracking in fluorescence images of cells doped with taxol and nocodazole

In this paper we describe an algorithm that performs automatic detection and tracking of astral microtubules in fluorescence confocal images. This sub-population of microtubules only exists during and immediately before mitosis and aids in the spindle orientation by connecting it to the cell cortex. Anomalies in their dynamic behaviour play a causal role in many diseases, such as development disorders and cancer. The main novelty of the proposed algorithm lies in the fact it provides a fully automated estimation of parameters related to microtubule dynamic instability (growth velocity, track length and track lifetime), and helps in understanding the effects of intermediate drug concentrations. Its performance has been objectively assessed using publicly available synthetic data and largely employed metrics. Moreover, we present experiments addressing cell cultures doped with different concentrations of taxol and nocodazole. Such drugs are known to suppress the microtubule dynamic instability, but their effects at intermediate concentrations are not completely assessed. The algorithm been compared with other stateof- the-art approaches, tested on consistent real datasets. The results are encouraging in terms of performance, robustness and simplicity of use, and the algorithm is now routinely employed in our Department of Molecular Biotechnology

Automatic approach for spot detection in microscopy imaging based on image processing and statistical analysis

Abstract: In biological research, fluorescence microscopy has become one of the vital tools used for observation, allowing researchers to study, visualise and image the details of intracel-lular structures which result in better understanding of biology. However, analysis of large numbers of samples is often required to draw statistically verifiable conclusions. Automated methods for analysis of microscopy image data make it possible to handle large datasets, and at the same time reduce the risk of bias imposed by manual techniques in the image analysis pipeline. This work covers automated methods for extracting quan-titative measurements from microscopy images, enabling the detection of spots resulting from different experimental conditions. The work resulted in four main significant con-tributions developed around the microscopy image analysis pipeline. Firstly, an investiga-tion into the importance of spot detection within the automated image analysis pipeline is conducted. Experimental findings show that poor spot detection adversely affected the remainder of the processing pipeline...D.Ing. (Electrical and Electronic Engineering