Automated Morphological Analysis from Medical Images

Overview

This project focused on automating morphological measurements from 3D medical images using Python.
The objective was to replace manual, time-consuming measurements with reliable and reproducible computational workflows, while keeping expert validation through visual review.

Key Features

• Automated Measurement Extraction
  Built Python scripts to compute metrics such as length, width, circumference, and volume from 3D segmentations.

• Repeatability & Reproducibility Analysis
  Implemented analysis workflows to quantify measurement consistency across repeated runs and subjects.

• Tool Integration for Visual Validation
  Integrated scripting with 3D Slicer to visualize outputs, validate bounding boxes, and support expert review.

• Structured Outputs for Analysis
  Exported clean datasets suitable for downstream statistics and reporting.

Technologies Used

• Python, NumPy, Pandas
• Matplotlib, Seaborn
• 3D Slicer (Python API), VTK

Challenges and Learnings

Ensuring anatomical relevance required careful handling of reference frames and systematic visual verification.
This project reinforced best practices around robust Python code in research-critical contexts: reproducibility, traceability, and validation loops.

Outcome

The automation significantly reduced manual measurement time while improving consistency and transparency.
The workflow scales to larger datasets and provides a strong base for future extensions such as statistical modeling or ML-based analysis.