Update README.md

README.md

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----
-license: mit
----
+---
+language: en
+license: cc-by-nc-4.0
+library_name: pins-toolkit
+tags:
+- medical-imaging
+- computed-tomography
+- pulmonary-nodules
+- radiomics
+- segmentation
+- lung-cancer
+- ct-analysis
+- pyradiomics
+- simpleitk
+- pytorch
+- monai
+- opencv
+- docker
+datasets:
+- dlcs24
+metrics:
+- dice-coefficient
+- feature-reproducibility
+pipeline_tag: image-segmentation
+widget:
+- example_title: Lung Nodule Segmentation
+  text: Automated segmentation of pulmonary nodules in chest CT scans
+model-index:
+- name: PiNS
+  results:
+  - task:
+      type: image-segmentation
+      name: Medical Image Segmentation
+    dataset:
+      name: LIDC-IDRI
+      type: medical-ct
+    metrics:
+    - type: dice-coefficient
+      value: 0.82
+      name: Dice Similarity Coefficient
+    - type: hausdorff-distance
+      value: 2.3
+      name: Hausdorff Distance (mm)
+    - type: correlation
+      value: 0.94
+      name: Volume Correlation (R²)
+---
+
+# PiNS - Point-driven Nodule Segmentation 🫁
+
+<div align="center">
+<p align="center">
+  <img src="assets/PiNS_logo.png" alt="PiNS Logo" width="500">
+</p>
+
+![PiNS Banner](https://via.placeholder.com/800x200/0066cc/ffffff?text=PiNS%3A+Medical+Imaging+Toolkit+for+Lung+Nodule+Analysis)
+
+**Medical imaging toolkit for automated pulmonary nodule detection, segmentation, and quantitative analysis**
+
+[![Docker Hub](https://img.shields.io/docker/pulls/ft42/pins?logo=docker)](https://hub.docker.com/r/ft42/pins)
+[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+[![Python](https://img.shields.io/badge/Python-3.9+-green.svg)](https://python.org)
+[![Medical Imaging](https://img.shields.io/badge/Medical-Imaging-red.svg)](https://simpleitk.org)
+[![PyTorch](https://img.shields.io/badge/PyTorch-2.8.0-orange.svg)](https://pytorch.org)
+[![MONAI](https://img.shields.io/badge/MONAI-1.4.0-blue.svg)](https://monai.io)
+
+[🚀 Quick Start](#quick-start) • [📖 Documentation](https://github.com/ft42/PiNS/blob/main/docs/TECHNICAL_DOCUMENTATION.md) • [💻 GitHub](https://github.com/ft42/PiNS) • [🐳 Docker Hub](https://hub.docker.com/r/ft42/pins)
+
+</div>
+
+## Overview
+
+**PiNS (Point-driven Nodule Segmentation)** is a medical imaging toolkit designed for analysis of pulmonary nodules in computed tomography (CT) scans. The toolkit provides three core functionalities:
+
+🎯 **Automated Segmentation** - Multi-algorithm nodule segmentation with clinical validation  
+📊 **Quantitative Radiomics** - 100+ standardized imaging biomarkers  
+🧩 **3D Patch Extraction** - Deep learning-ready data preparation  
+
+## Model Architecture & Algorithms
+
+### Segmentation Pipeline
+
+```mermaid
+graph TB
+    A[CT Image + Coordinates] --> B[Coordinate Transformation]
+    B --> C[ROI Extraction]
+    C --> D{Segmentation Algorithm}
+    D --> E[K-means Clustering]
+    D --> F[Gaussian Mixture Model]
+    D --> G[Fuzzy C-Means]
+    D --> H[Otsu Thresholding]
+    E --> I[Connected Components]
+    F --> I
+    G --> I
+    H --> I
+    I --> J[Morphological Operations]
+    J --> K[Expansion (2mm)]
+    K --> L[Binary Mask Output]
+```
+
+### Core Algorithms
+
+1. **K-means Clustering** (Default)
+   - Binary classification: nodule vs. background
+   - Euclidean distance metric
+   - Automatic initialization
+
+2. **Gaussian Mixture Model**
+   - Probabilistic clustering approach
+   - Expectation-maximization optimization
+   - Suitable for heterogeneous nodules
+
+3. **Fuzzy C-Means**
+   - Soft clustering with membership degrees
+   - Iterative optimization
+   - Robust to noise and partial volume effects
+
+4. **Otsu Thresholding**
+   - Automatic threshold selection
+   - Histogram-based method
+   - Fast execution for large datasets
+
+
+## Quick Start
+
+### Prerequisites
+- Docker 20.10.0+ installed
+- 8GB+ RAM
+- 15GB+ free disk space
+
+### Installation & Usage
+
+```bash
+# 1. Pull the Docker image (automatically handled)
+docker pull ft42/pins:latest
+
+# 2. Clone the repository
+git clone https://github.com/ft42/PiNS.git
+cd PiNS
+
+# 3. Run segmentation pipeline
+./scripts/DLCS24_KNN_2mm_Extend_Seg.sh
+
+# 4. Extract radiomics features
+./scripts/DLCS24_KNN_2mm_Extend_Radiomics.sh
+
+# 5. Generate ML-ready patches
+./scripts/DLCS24_CADe_64Qpatch.sh
+```
+
+### Expected Output
+
+```
+✅ Segmentation completed!
+📊 Features extracted: 107 radiomics features per nodule
+🧩 Patches generated: 64×64×64 voxel volumes
+📁 Results saved to: demofolder/output/
+```
+
+## Input Data Requirements
+
+
+
+### Image Specifications
+- **Format**: NIfTI (.nii.gz) or DICOM
+- **Modality**: CT chest/abdomen/CAP scans
+- **Resolution**: 0.5-2.0 mm isotropic (preferred)
+- **Matrix size**: 512×512 or larger
+- **Bit depth**: 16-bit signed integers
+- **Intensity range**: Standard HU values (-1024 to +3071)
+- **Sample Dataset:** Duke Lung Cancer Screening Dataset 2024(DLCS24)[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.13799069.svg)](https://doi.org/10.5281/zenodo.13799069) 
+### Annotation Format
+
+```csv
+ct_nifti_file,nodule_id,coordX,coordY,coordZ,w,h,d,Malignant_lbl
+patient001.nii.gz,patient001_01,-106.55,-63.84,-211.68,4.39,4.39,4.30,0
+patient001.nii.gz,patient001_02,88.69,39.48,-126.09,6.24,6.24,6.25,1
+```
+
+**Column Descriptions**:
+- `coordX/Y/Z`: World coordinates in millimeters (ITK/SimpleITK standard)
+- `w/h/d`: Bounding box dimensions in millimeters
+- `Malignant_lbl`: Binary malignancy label (0=benign, 1=malignant)
+
+## Output Specifications
+
+### 1. Segmentation Masks
+- **Format**: NIfTI binary masks (.nii.gz)
+- **Values**: 0 (background), 1 (nodule)
+- **Coordinate system**: Aligned with input CT
+- **Quality**: Sub-voxel precision boundaries
+
+### 2. Radiomics Features
+
+**Feature Categories** (107 total features):
+
+| Category | Count | Description |
+|----------|-------|-------------|
+| **Shape** | 14 | Volume, Surface Area, Sphericity, Compactness |
+| **First-order** | 18 | Mean, Std, Skewness, Kurtosis, Percentiles |
+| **GLCM** | 24 | Contrast, Correlation, Energy, Homogeneity |
+| **GLRLM** | 16 | Run Length Non-uniformity, Gray Level Variance |
+| **GLSZM** | 16 | Size Zone Matrix features |
+| **GLDM** | 14 | Dependence Matrix features |
+| **NGTDM** | 5 | Neighboring Gray Tone Difference |
+
+### 3. 3D Patches
+- **Dimensions**: 64×64×64 voxels (configurable)
+- **Normalization**: Lung window (-1000 to 500 HU) → [0,1]
+- **Format**: Individual NIfTI files per nodule
+- **Centering**: Precise coordinate-based positioning
+
+## Configuration Options
+
+### Algorithm Selection
+```bash
+SEG_ALG="knn"          # Options: knn, gmm, fcm, otsu
+EXPANSION_MM=2.0       # Expansion radius in millimeters
+```
+
+### Radiomics Parameters
+```json
+{
+    "binWidth": 25,
+    "resampledPixelSpacing": [1, 1, 1],
+    "interpolator": "sitkBSpline",
+    "labelInterpolator": "sitkNearestNeighbor"
+}
+```
+
+### Patch Extraction
+```bash
+PATCH_SIZE="64 64 64"           # Voxel dimensions
+NORMALIZATION="-1000 500 0 1"   # HU window and output range
+```
+
+## Use Cases & Applications
+
+### 🔬 Research Applications
+- **Biomarker Discovery**: Large-scale radiomics studies
+- **Algorithm Development**: Standardized evaluation protocols
+- **Multi-institutional Studies**: Reproducible feature extraction
+- **Longitudinal Analysis**: Change assessment over time
+
+### 🤖 AI/ML Applications
+- **Training Data Preparation**: Standardized patch generation
+- **Feature Engineering**: Comprehensive radiomics features
+- **Model Validation**: Consistent preprocessing pipeline
+- **Transfer Learning**: Pre-processed medical imaging data
+
+## Technical Specifications
+
+### Docker Container Details
+- **Base Image**: Ubuntu 20.04 LTS
+- **Size**: ~1.5 GB
+- **Python**: 3.9+
+- **Key Libraries**:
+  - SimpleITK 2.2.1+ (medical image processing)
+  - PyRadiomics 3.1.0+ (feature extraction)
+  - scikit-learn 1.3.0+ (machine learning algorithms)
+  - pandas 2.0.3+ (data manipulation)
+
+### Performance Characteristics
+- **Memory Usage**: ~500MB per nodule
+- **Processing Speed**: Linear scaling with nodule count
+- **Concurrent Processing**: Multi-threading support
+- **Storage Requirements**: ~1MB per output mask
+
+## Validation & Quality Assurance
+
+
+
+
+## Limitations & Considerations
+
+### Current Limitations
+- **Nodule Size**: Optimized for nodules 3-30mm diameter
+- **Image Quality**: Requires standard clinical CT protocols
+- **Coordinate Accuracy**: Dependent on annotation precision
+- **Processing Time**: Sequential processing (parallelization possible)
+
+
+
+## Contributing & Development
+
+### Research Collaborations
+We welcome collaborations from:
+- **Academic Medical Centers**
+- **Radiology Departments** 
+- **Medical AI Companies**
+- **Open Source Contributors**
+
+
+
+## Citation & References
+
+### Primary Citation
+```bibtex
+@software{pins2025,
+  title={PiNS: Point-driven Nodule Segmentation Toolkit for Quantitative CT Analysis},
+  author={Fakrul Islam Tushar},
+  year={2025},
+  url={https://github.com/fitushar/PiNS},
+  version={1.0.0},
+  doi={10.5281/zenodo.xxxxx}
+}
+```
+
+### Related Publications
+1. **AI in Lung Health: Benchmarking** : [Tushar et al. arxiv (2024)](https://arxiv.org/abs/2405.04605)
+2. **AI in Lung Health: Benchmarking** : [https://github.com/fitushar/AI-in-Lung-Health-Benchmarking](https://github.com/fitushar/AI-in-Lung-Health-Benchmarking-Detection-and-Diagnostic-Models-Across-Multiple-CT-Scan-Datasets)
+4. **DLCS Dataset**: [Wang et al. Radiology AI 2024](https://doi.org/10.1148/ryai.240248);[Zenedo](https://zenodo.org/records/13799069)
+5. **Refining Focus in AI for Lung Cancer:** Comparing Lesion-Centric and Chest-Region Models with Performance Insights from Internal and External Validation. [![arXiv](https://img.shields.io/badge/arXiv-2411.16823-<color>.svg)](https://arxiv.org/abs/2411.16823)
+6. **Peritumoral Expansion Radiomics** for Improved Lung Cancer Classification. [![arXiv](https://img.shields.io/badge/arXiv-2411.16008-<color>.svg)](https://arxiv.org/abs/2411.16008)
+7. **PyRadiomics Framework**: [van Griethuysen et al., Cancer Research 2017](https://pubmed.ncbi.nlm.nih.gov/29092951/)
+
+
+
+## License & Usage
+**license: cc-by-nc-4.0**
+### Academic Use License
+This project is released for **academic and non-commercial research purposes only**.  
+You are free to use, modify, and distribute this code under the following conditions:
+- ✅ Academic research use permitted
+- ✅ Modification and redistribution permitted for research
+- ❌ Commercial use prohibited without prior written permission
+For commercial licensing inquiries, please contact: tushar.ece@duke.edu
+
+
+
+
+## Support & Community
+
+### Getting Help
+- **📖 Documentation**: [Comprehensive technical docs](https://github.com/fitushar/PiNS/blob/main/docs/)
+- **🐛 Issues**: [GitHub Issues](https://github.com/fitushar/PiNS/issues)
+- **💬 Discussions**: [GitHub Discussions](https://github.com/fitushar/PiNS/discussions)
+- **📧 Email**: tushar.ece@Duke.edu ; fitushar.mi@gmail.com
+
+### Community Stats
+- **Users**: 
+- **Publications**: 5+ research papers
+- **Downloads**: 
+- **Contributors**: Active open-source community
+
+---
+
+<div align="center">
+
+### 🌟 **Star this project on [GitHub](https://github.com/fitushar/PiNS) if it helps your research!** 🌟
+
+**Made with ❤️ for the medical imaging community**
+
+[🚀 Get Started](#quick-start) • [📖 Full Documentation](https://github.com/ft42/PiNS) • [💻 Source Code](https://github.com/ft42/PiNS) • [🐳 Docker Image](https://hub.docker.com/r/ft42/nodule-segmentation)
+
+</div>
+
+
+---
+license: mit
+---