Zack4DEV/cellpose_sam_spheres
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📊 Cell Seeding Efficiency Measurement with Cellpose + SAM This project analyzes live cell imaging time-series data using Cellpose, SAM, and Microsam to classify cell states and compute seeding efficiency metrics. --- 📁 Folder Structure - data/raw/: Original input .tif images - data/processed/: Segmentations, masks, or extracted features - results/masks/: Per-frame labeled masks for 4 classes - results/videos/: AVI visualizations (overlays + masks) - results/metrics.csv: Table of cell count, area, brightness per frame - logs/: Daily notes and changelogs --- 🧪 Classification Targets 1. Circular cells (initial frames) 2. Fixed cells (midpoint) 3. Dead circular cells (post-fixation) 4. Fragments or abnormal detections --- ⚙ Workflow 1. Preprocess .tif using Cellpose+SAM 2. Segment and classify per frame 3. Track and extract brightness, area, count 4. Output: - Masked videos - Combined overlay videos - CSV with per-frame stats --- ▶ Output - metrics.csv: frame, class, avg_brightness, avg_area, count - AVI videos with overlayed masks and raw frames --- 🧠 Dependencies Requirements.txt Py packages for the Python environment setup. --- 📆 Logs Daily logs are maintained in logs/daily.md