Medical AI · Segmentation
Brain Tumor Segmentation
U-Net encoder-decoder architecture for pixel-level semantic segmentation of brain tumors from MRI volumes.
The Challenge
Precision
at the voxel level.
Accurate brain tumor segmentation from MRI scans is critical for surgical planning, radiotherapy targeting, and longitudinal monitoring. Manual segmentation by radiologists is time-consuming and subject to inter-observer variability. Automated segmentation must achieve pixel-level precision with clear boundary delineation across heterogeneous tumor morphologies — including enhancing tumor, peritumoral edema, and necrotic core regions.
The Architecture
Encoder-decoder
segmentation.
U-Net Architecture
Symmetric encoder-decoder with skip connections that preserve spatial resolution across the network. The contracting path captures context via successive downsampling, while the expansive path enables precise localization through upsampling and skip concatenation.
Multi-Class Segmentation
Multi-class output head for simultaneous classification of tumor sub-regions: enhancing tumor, surrounding edema, and necrotic tissue. Dice loss optimization handles severe class imbalance inherent in medical imaging datasets.
Data Augmentation Pipeline
Rotation, elastic deformation, intensity variation, and random cropping to expand the effective training set. Critical for medical imaging where labeled data is scarce and expensive to acquire.
The Outcome
Clinical-grade
delineation.
The model achieves precise tumor boundary delineation across diverse MRI volumes. Skip connections preserve fine-grained spatial detail that pure convolutional approaches lose, enabling accurate segmentation of small tumor regions. The system is designed for integration into clinical radiology workflows as a decision-support tool.