CZII-CryoET-Object-Identification

This project leverages machine learning models to automatically identify and classify protein complexes in cryo-electron tomography (cryoET) images, enabling large-scale structural analysis of cellular components and contributing to advances in biomedical research

🧬 Cryo-ET Protein Object Identification

A high-performance deep-learning pipeline for identifying protein complexes in 3D cryo-electron tomography (cryoET) tomograms Designed for clarity, reproducibility, and professional research use

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📘 Overview

Cryo-electron tomography generates high-resolution 3D views of cellular environments These volumes contain thousands of tightly packed protein complexes that need automated identification for biological and medical research

This project provides a complete machine-learning workflow for:

• 3D tomogram preprocessing.
• Feature extraction.
• Multi-class model training.
• Inference and predictions.
• Clean reproducible research structure.

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🗂️ Table of Contents

• Features
• Project Structure
• Dataset
• Modeling Approach
• Notebook
• Usage
• ModelPerformance
• References
• License

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⚡ Features

✔️ 3D tomogram ingestion & preprocessing ✔️ Deep-learning based multi-class identification ✔️ Modular and extendable source code ✔️ Kaggle-compatible training & inference ✔️ GPU-ready workflow ✔️ Clear, research-friendly architecture

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🗂️ Project Structure

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🧬 Dataset

🔗 CZII Cryo-ET Dataset (Kaggle)
https://www.kaggle.com/competitions/czii-cryo-et-object-identification/data

Dataset Includes:

Component	Description
Tomograms	High-resolution 3D cryo-ET volumes
Masks	Protein complex segmentations
Metadata	Tomogram & annotation info
Classes	5 professionally curated protein complex categories

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🧠 Modeling Approach

1. Preprocessing

preprocessing inlcude steps as:

• Volume normalization
• 3D grid or patch generation
• Noise reduction
• Augmentation (optional)

2. Architecture

the architechture is as follows:

• 3D-CNN / adapted YOLO-3D style detector
• Multi-class classifier
• Spatial feature extraction

3. Training Workflow

following shows the workflow:

• Balanced dataloading
• Weighted loss for rare classes
• GPU-trained models

4. Evaluation Metrics

Following metrices are used:

• Accuracy
• F1-score
• IoU
• Per-class recall

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🧪 Kaggle Notebook

📓 Full notebook implementation (training + inference):
https://www.kaggle.com/code/eeshakhanzadi/object-czii-competition?scriptVersionId=219427511

Includes:

Stage	Status
Data Loading	✔️
Preprocessing	✔️
Model Training	✔️
Eval Metrics	✔️
Prediction Pipeline	✔️

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🚀 Usage

1. Clone

git clone https://github.com/YOUR_USERNAME/CryoET-Object-Identification.git
cd CryoET-Object-Identification

2. Train Model

python src/train.py

3. Run Inference

python src/inference.py --input sample_data/example_tomo.mrc

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🏆 Model-Performance

📎 References

CryoET Dataset: https://www.kaggle.com/competitions/czii-cryo-et-object-identification/data

Training Notebook: https://www.kaggle.com/code/eeshakhanzadi/object-czii-competition

📄 License

Licensed under the MIT License.

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