3D Tumor Segmentation (College Project)
Volumetric deep learning system for segmenting tumor regions from 3D MRI/CT scan data using a 3D U-Net architecture.
Project Snapshot
Role
Developer
Organization
JECRC University
Timeline
2023–2024
Status
Completed
Deployment
Academic / Research
Tech Stack
Impact & Results
- 3D tumor segmentation on volumetric medical scans
A volumetric deep learning system developed as a college project for segmenting tumor regions from 3D MRI/CT scan data using a 3D U-Net architecture.
The Problem
Medical imaging relies heavily on volumetric data (like MRI or CT scans). Accurately segmenting tumor regions from 3D volumes is a critical and complex task requiring specialized volumetric deep learning architectures rather than standard 2D image models.
The Solution
A 3D image segmentation pipeline that ingests volumetric medical scans and outputs precise segmentations of tumor regions using a 3D U-Net.
Architecture & Implementation
Modeling: Trained a 3D U-Net architecture to process and segment volumetric data.
Evaluation: Evaluated the model using standard medical imaging metrics such as Dice score and Intersection over Union (IoU) on benchmark datasets.
Data Used: Volumetric medical image scans (MRI/CT).
Tech Stack: Python, PyTorch, TensorFlow, 3D U-Net, NiBabel, ITK.
Challenges & Solutions
Computational Cost: Managing the massive memory and computational requirements of training 3D CNNs on consumer hardware.
Data Variance: Handling varied contrast levels, resolutions, and artifacts in medical MRI/CT scans.
Architecture: Adapting standard 2D U-Net architectures to function effectively in 3D space.
Key Takeaways
3D U-Net architectures and volumetric deep learning.
Handling specialized medical data formats using NiBabel and ITK.
Evaluating models using strict medical imaging metrics (Dice score, IoU).