3D Organoid Segmentation and Feature Extraction in Python

Learn to segment cells and nuclei in 3D organoid images using Cellpose, perform quality control, and extract quantitative features for analysis in this comprehensive Python tutorial. Master the complete pipeline from raw TIFF stacks to CSV feature tables by segmenting nuclei and cells in 3D using Cellpose deep learning models, matching each nucleus to its parent cell, applying quality control filters to remove artifacts, and extracting morphological, intensity, topology, and shape features per cell. Transform raw 3D fluorescence microscopy images into analyzable data through automated segmentation and feature extraction, bridging the gap between raw microscopy data and structured feature tables used in subsequent analysis. Work with PDAC organoid images from Ong et al., Nature Methods 2025, using provided nuclei and cell channel TIFF files, and develop production-ready code for analyzing organoid datasets with complete notebook code available for implementation.