Advanced Pixel Classification and Segmentation in QuPath скачать в хорошем качестве

Advanced Pixel Classification and Segmentation in QuPath

Move from manual drawing to automated discovery. In this FS2K session, we dive into the mechanics of QuPath’s Pixel Classifier. You will learn how to transition from viewing pixels as colors to analyzing them as numerical data, allowing you to train machine learning models that recognize complex biological structures. Key concepts: ☑️ Object-Oriented Analysis: Why reducing pixels to objects is the secret to managing large-scale imaging data. ☑️ Trained vs. Untrained Learning: When to use Classification to find known phenotypes and when to use Clustering for discovery. ☑️ Pixel Classifier Features: Applying Gaussian, Laplacian, and Gradient filters to help the computer "see" edges and blobs. ☑️ Workflow Optimization: Leveraging "Live Prediction," multi-image training, and automated size filters to clean up noisy data. 00:00 - Introduction to Pixels and Bioimage Objects 01:37 - Defining Segmentation: From Pixels to Objects 02:22 - Methods: Manual, Threshold, and Machine Learning 04:04 - Programming vs. Machine Learning Logic 04:52 - Classification vs. Clustering Strategies 06:33 - Impact of Data Quality and Normalization 08:08 - Using ChatGPT for QuPath Scripting: A Warning 09:05 - Exporting Views and Setting Zoom 10:50 - Training the Pixel Classifier: Polyline vs. Brush 12:15 - Diverse Annotations for Robust Learning 13:17 - Setting Up Classes and Live Prediction 15:33 - Correcting Mistaken Classifications 16:44 - Feature Selection: Choosing Channels and Detail 18:40 - Understanding Filters: Gaussian, Laplacian, and Gradient 20:46 - Multi-Image Training and Extrapolation 22:36 - Balancing Training Data Classes (Pie Chart Analysis) 24:43 - Biologically Real Results vs. AI Mimicry 26:47 - Understanding the JSON Classifier Structure 28:02 - Project Backups and Data Safety 31:19 - Creating Final Objects from Classification 32:18 - Noise Reduction and Size Filtering 36:06 - Annotation Measurements and Result Comparison Step-by-Step Training Guide: https://saramcardle.github.io/FS2K/Se...