Deep Learning in Medical Imaging

Medical imaging has been revolutionized by deep learning techniques. This research focuses on applying advanced neural network architectures to the challenging problem of 3D cellular image segmentation.

Architectures Used

U-Net++

U-Net++ is an enhanced version of the original U-Net architecture, featuring nested and dense skip connections. This design allows for more flexible feature aggregation and improved segmentation accuracy.

Key advantages:

• Better gradient flow through nested connections
• Multi-scale feature extraction
• Reduced semantic gap between encoder and decoder

Mask R-CNN

Mask R-CNN extends Faster R-CNN by adding a branch for predicting segmentation masks. This instance segmentation approach is particularly useful for identifying individual cellular structures.

Applications:

• Cell counting and classification
• Tumor boundary detection
• Tissue morphology analysis

3D Cellular Spheroid Segmentation

Our research focuses specifically on in-vitro cellular spheroids, which are 3D cell culture models used extensively in drug discovery and cancer research.

Challenges

• Complex 3D geometry
• Variable illumination conditions
• Overlapping cellular structures
• Limited annotated training data

Solutions

1. Data Augmentation: Rotation, scaling, and intensity transformations
2. Transfer Learning: Pre-training on related medical imaging datasets
3. Genetic Algorithm Optimization: Fine-tuning hyperparameters for optimal performance

Results

Our combined approach of genetic algorithms and deep learning has shown significant improvements in:

• Segmentation accuracy (Dice coefficient improvement of ~15%)
• Processing speed for real-time applications
• Robustness to image quality variations

This research forms part of my Master's thesis at Mohammed V University of Rabat.