Advanced Pixel Classification and Segmentation in QuPath

Описание: 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...