Biology subjects

Al Sa'd, M.

Publications and source records attributed to Al Sa'd, M..

3 recordsLinked to original sources

bioRxiv2025

Robust Consensus Nuclear and Cell Segmentation

Cell segmentation is a crucial step in numerous biomedical imaging endeavors; so much so that the community is flooded with publicly available, state-of-the-art segmentation techniques ready for out of the box use. Assessing the virtues and limitations of each method on a tissue sample set and then selecting the optimum method for each research objective and input image is a time consuming and exacting task that often monopolizes the resources of biologists, biochemists, immunologists and pathologists; despite not being their project primary research goal. In this work, we present a segmentation software wrapper, coined CellSampler, which runs a selection of established segmentation methods and then combines their individual segmentation masks into a single optimized mask. This, so called uber mask, selects the best of the established masks across local neighborhoods within the image, where the neighborhood size and the statistical measure which determines the qualitative term best are both chosen by the user.

bioinformatics↗

bioRxiv2021

Exploration and analysis of molecularly annotated, 3D models of breast cancer at single-cell resolution using virtual reality

A set of increasingly powerful approaches are enabling spatially resolved measurements of growing numbers of molecular features in biological samples. While important insights can be derived from the two-dimensional data that many of these technologies generate, it is clear that extending these approaches into the third and fourth dimensions will magnify their impact. Realizing biological insights from datasets where thousands to millions of cells are annotated with tens to hundreds of parameters in space will require the development of new computational and visualization strategies. Here, we describe Theia, a virtual reality-based platform, which enables exploration and analysis of either volumetric or segmented, molecularly-annotated, three-dimensional datasets, with the option to extend the analysis to time-series data. We also describe our pipeline for generating annotated 3D models of breast cancer and supply several datasets to enable users to explore the utility of Theia for understanding cancer biology in three dimensions.

cancer biology↗

bioRxiv2021

The Imaging and Molecular Annotation of Xenografts and Tumours (IMAXT) High Throughput Data and Analysis Infrastructure

With the aim of producing a 3D representation of tumours, IMAXT uses a large variety of modalities in order to acquire tumour samples and produce a map of every cell in the tumour and its host environment. With the large volume and variety of data produced in the project we develop automatic data workflows and analysis pipelines and introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located instead of bringing data to their local computers. Here we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumour models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT.

cancer biology↗