PhenoPixel is a full-stack web application for single-cell analysis of time-lapse microscopy data, specifically designed for mother machine microfluidics experiments. The system ingests Nikon ND2 files, segments cells using both classical image processing (Canny, PCA, centerline fitting) and a bundled machine learning ensemble (Weighted KNN + LogisticRegressionGD), and stores extracted features in per-experiment SQLite databases. It provides an interactive React frontend for manual annotation, bulk population analysis (cell length, area, fluorescence heatmaps, map256), and training dataset creation for supervised models. The architecture follows a modular FastAPI backend with RESTful APIs, async file storage, and background processing via ProcessPoolExecutor for CPU-intensive tasks, served alongside a React 19 SPA built with Chakra UI and Vite. Key modules include the cell extraction pipeline, database manager with overlay rendering, bulk engine for population-level metrics, graph engine for aggregation scores, and a dedicated mother machine workflow with Cellpose-SAM segmentation. The backend also includes a Model Context Protocol (MCP) server for AI-assisted repository navigation. While primarily an application, PhenoPixel has strong data research characteristics—it generates quantitative biological measurements, bundles a trained auto-annotation model with evaluation, and includes detailed algorithmic documentation in Docusaurus with LaTeX equations.