Part1 - NEUROSEG - Intro and functionalities скачать в хорошем качестве

Part1 - NEUROSEG - Intro and functionalities

Introduction and showcase of the image analysis functionalities of the NEUROSEG Jupyter Notebook collection found on Github. This repository provides tools in the form of interactive Jupyter Notebooks to define cell populations based on the presence or absence of multiple fluorescent markers in multichannel 3D-stacks or 2D-images. The pipeline has been designed to be used with brain and organoid tissue sections but also works for cell cultures. Analysis can be performed on the whole image by default or on multiple user-defined ROIs. It also allows you to extract average marker intensity signal from nuclei and cytoplasm to easily calculate nuclear translocation ratios. Timestamps: 0:00 – Introduction 0:30 – Tool functionality (define marker positive cells) 1:20 – Type of input images (multichannel 3D-stacks, MIP projections and 2D-images) 2:22 – Multichannel image analysis 2:50 – Nuclear segmentation with Stardist 3:13 – Region of interest (ROI definition) 3:22 – Cellular compartments (nucleus, cell, cytoplasm) 4:06 – Cell population definition overview 4:53 – Define cell populations using Average Intensity Measurements 8:12 – Define cell populations based on marker colocalization (morphological operations) 10:05 – Define cell populations based on a Pixel Intensity Random Forest Classifier (interactive) 11:11 – Pixel Classifier Results Keywords / Tags: #imageanalysis #microscopy #stardist #deeplearning #randomforest #research #brain #fluorescence #molecularbiology #python #bioimageanalysis #imageanalysis #computervision #napari