Automatic generation of synthetic datasets for digital pathology image analysis

The project is inspired by an actual problem of timing and accessibility in the analysis of histological samples in the health-care system. In this project, I address the problem of synthetic histological image generation for the purpose of training Neural Networks for the segmentation of real histological images. The collection of real histological human-labeled samples is a very time consuming and expensive process and often is not representative of healthy samples, for the intrinsic nature of the medical analysis. The method I propose is based on the replication of the traditional specimen preparation technique in a virtual environment. The first step is the creation of a 3D virtual model of a region of the target human tissue. The model should represent all the key features of the tissue, and the richer it is the better will be the yielded result. The second step is to perform a sampling of the model through a virtual tomography process, which produces a first completely labeled image of the section. This image is then processed with different tools to achieve a histological-like aspect. The most significant aesthetical post-processing is given by the action of a style transfer neural network that transfers the typical histological visual texture on the synthetic image. This procedure is presented in detail for two specific models: one of pancreatic tissue and one of dermal tissue. The two resulting images compose a pair of images suitable for a supervised learning technique. The generation process is completely automatized and does not require the intervention of any human operator, hence it can be used to produce arbitrary large datasets. The synthetic images are inevitably less complex than the real samples and they offer an easier segmentation task to solve for the NN. However, the synthetic images are very abundant, and the training of a NN can take advantage of this feature, following the so-called curriculum learning strategy

3D CNN methods in biomedical image segmentation

A definite trend in Biomedical Imaging is the one towards the integration of increasingly complex interpretative layers to the pure data acquisition process. One of the most interesting and looked-forward goals in the field is the automatic segmentation of objects of interest in extensive acquisition data, target that would allow Biomedical Imaging to look beyond its use as a purely assistive tool to become a cornerstone in ambitious large-scale challenges like the extensive quantitative study of the Human Brain. In 2019 Convolutional Neural Networks represent the state of the art in Biomedical Image segmentation and scientific interests from a variety of fields, spacing from automotive to natural resource exploration, converge to their development. While most of the applications of CNNs are focused on single-image segmentation, biomedical image data -being it MRI, CT-scans, Microscopy, etc- often benefits from three-dimensional volumetric expression. This work explores a reformulation of the CNN segmentation problem that is native to the 3D nature of the data, with particular interest to the applications to Fluorescence Microscopy volumetric data produced at the European Laboratories for Nonlinear Spectroscopy in the context of two different large international human brain study projects: the Human Brain Project and the White House BRAIN Initiative

A method for assessing the spatiotemporal resolution of Structured Illumination Microscopy (SIM)

A method is proposed for assessing the temporal resolution of Structured Illumination Microscopy (SIM), by tracking the amplitude of different spatial frequency components over time, and comparing them to a temporally-oscillating ground-truth. This method is used to gain insight into the performance limits of SIM, along with alternative reconstruction techniques (termed 'rolling SIM') that claim to improve temporal resolution. Results show that the temporal resolution of SIM varies considerably between low and high spatial frequencies, and that, despite being used in several high profile papers and commercial microscope software, rolling SIM provides no increase in temporal resolution over conventional SIM.Comment: 8 pages, 6 figures and 3 supplemental figure

Bioimage informatics in STED super-resolution microscopy

Optical microscopy is living its renaissance. The diffraction limit, although still physically true, plays a minor role in the achievable resolution in far-field fluorescence microscopy. Super-resolution techniques enable fluorescence microscopy at nearly molecular resolution. Modern (super-resolution) microscopy methods rely strongly on software. Software tools are needed all the way from data acquisition, data storage, image reconstruction, restoration and alignment, to quantitative image analysis and image visualization. These tools play a key role in all aspects of microscopy today – and their importance in the coming years is certainly going to increase, when microscopy little-by-little transitions from single cells into more complex and even living model systems. In this thesis, a series of bioimage informatics software tools are introduced for STED super-resolution microscopy. Tomographic reconstruction software, coupled with a novel image acquisition method STED< is shown to enable axial (3D) super-resolution imaging in a standard 2D-STED microscope. Software tools are introduced for STED super-resolution correlative imaging with transmission electron microscopes or atomic force microscopes. A novel method for automatically ranking image quality within microscope image datasets is introduced, and it is utilized to for example select the best images in a STED microscope image dataset.Siirretty Doriast

Towards all optical functional imaging of neuronal cells : development of a video rate multimodal laser scanning microscope

L'objectif premier de ce projet de maîtrise consiste à développer un nouveau microscope confocal multimodal rapide adapté à l'imagerie fonctionnelle des cellules neuronales. Un microscope a été dessiné, construit et testé au Centre de Recherche Université Laval - Robert-Giffard (CRULRG) à Québec. Ce mémoire présente les différents aspects du développement du microscope (design optique et électronique) ainsi que les résultats des tests d'imagerie effectués sur des neurones en culture. Selon le marqueur chimique ainsi que la modalité de microscopie utilisée, nous avons observé une corrélation entre les variations du signal optique et du potentiel transmembranaire ou de l'activité calciques des neurones. Cet outil ouvre la porte à de nouvelles experiences en permettant l'observation de l'activité neuronale dans un grand volume et de facon non-invasive, contrairement à la technique actuelle appelée 'patch-clamp'.The main goal of this master's project is to develop a novel video rate multimodal laser scanning microscope specifically designed to perform functional imaging of neuronal cells. A microscope was designed, built and tested at the Centre de Recherche Universite Laval - Robert Giffard (CRULRG) in Quebec City. This thesis summarizes the system implementation choices we made (optical design, electronics) in order to achieve our imaging goals. Results showing the correlation between the optical signal variations and the cell transmembrane potential or calcic activity changes are presented. This tool opens the door to new experiments, allowing for non-invasive observations of cell activity over a large volume thus overcoming important limitations of the established 'patch-clamp' technique