Automated 3D Segmentation of the Root Meristem
🔬 Project Overview
This project addresses the challenge of analyzing complex 3D cellular structures in the Arabidopsis thaliana root apical meristem. Currently under development, the pipeline automates the processing of multi-channel confocal stacks to extract precise geometric and temporal data.
🛠️ Technical Workflow
1. Multi-Channel Processing
The pipeline handles raw .nd2 (Nikon) files, synchronizing three key biological reporters:
- Cell Wall (e.g., PI/Renaissance): Defines the structural scaffold for geometric analysis.
- Dual Cell-Cycle Reporters: Visualizes nuclear dynamics at different phases (G1/S/G2/M), allowing for the mapping of proliferation zones.
2. AI-Driven Segmentation
We leverage state-of-the-art Convolutional Neural Networks (CNNs) to overcome the limitations of traditional thresholding:
- PlantSeg: Provides robust 3D segmentation of cell boundaries from cell wall markers.
- Cellpose: Delivers high-fidelity nuclear segmentation, even in dense meristematic regions.
3. Curation & Refinement
- Napari Integration: A custom Python-based GUI for real-time proofreading and manual correction.
- Post-Processing: Scripts to calculate cell volumes, surface areas, and spatial distribution of cell-cycle states.
🚀 Key Outcomes
- Automation: Drastic reduction in manual annotation time for large datasets.
- Precision: High-resolution mapping of cell identity and proliferation patterns.
- Scale: Enables the quantitative comparison of wild-type vs. mutant phenotypes at a single-cell level.
💻 Stack & Tools
| Category | Tools | | :— | :— | | Languages | Python, MATLAB | | DL Frameworks | PyTorch (PlantSeg), Cellpose | | Visualization | Napari, Fiji/ImageJ | | Bioinformatics | ND2SDK, NumPy, SciPy |