$ show SchmollerLab/Cell_ACDC
Cell-ACDC is a desktop GUI application for bioimage time-lapse analysis, purpose-built for cell segmentation, tracking, and cell cycle annotation in live-cell imaging data. The application is built on PyQt5/PyQt6 with pyqtgraph for visualization, and exposes both a full graphical interface and a headless CLI mode via console entry points (`cellacdc`, `acdc`, `Cell-ACDC`). It includes Fiji/ImageJ macros for converting raw microscopy files into the expected folder structure, Cython-accelerated extensions for performance, and a QObject-based worker/signal architecture for running long segmentation and tracking tasks asynchronously with progress reporting and pause/resume control. The codebase is organized around a monolithic `dataPrepWin` GUI class that mixes UI construction, file I/O, worker management, and domain logic, alongside more modular components: `core.py` provides image-analysis and segmentation post-processing helpers (nearest-point matching, convex-hull calculations, label filtering), `whitelist.py` implements an ID filtering mechanism with original label preservation, and `workers.py` defines ~30 reusable signals for progress, error handling, and task control. The application depends on external tools including Java (for some segmentation plugins), PyTorch (for deep-learning-based segmentation), and a rigid folder structure with hardcoded paths. The repository shows active maintenance with CI workflows for both PyQt5 and PyQt6, Cython build automation, and issue templates. However, it exhibits technical drift: a deprecated PyTorch conda channel is referenced, Java version compatibility issues exist, and the dual Qt5/Qt6 support increases test surface. The codebase has moderate coupling and hardcoded settings (e.g., `QSettings('schmollerlab', 'acdc_dataPrep')`), but the core algorithmic components are sufficiently isolated for reuse in other bioimage analysis pipelines.
The LLM identified 4 files worth highlighting for reuse:
Contains low-level image-analysis helpers including nearest-point matching between objects, convex-hull/convexity-defect calculations, and label filtering by IDs. These geometry routines are self-contained and can be extracted for any object-tracking or segmentation post-processing pipeline. Also includes cell/sub-object deletion logic that preserves original label identities.
Implements a whitelist mechanism to include/exclude objects across frames while preserving original label IDs. This is a reusable pattern for any tracking or annotation system that needs to filter objects without renumbering, applicable beyond cell biology to general multi-object tracking and video annotation tools.
Defines QObject-based worker classes (e.g., SegForLostIDsWorker, AutoSaveWorker) with ~30 standard signals (progress, finished, critical, initProgressBar) for managing long-running segmentation and tracking tasks. The signal/slot pattern for pause/resume/abort control is a solid template for building responsive Qt applications with complex background tasks, and the signal names are generic enough to be reused across domains.
Provides utility functions for downloading model weights (download_model), checking/installing Python packages (check_install_package), and managing optional dependencies like javabridge and torch. This is a reusable pattern for applications that need to lazily fetch ML model weights or manage optional heavy dependencies at runtime, solving a common problem in scientific software distribution.