DEEP LEARNING · MEDICAL IMAGING
A collaborative deep-learning application using U-Net segmentation to identify anatomical structures within 3D medical CT scans.
DeepMedSeg was developed as a team project exploring the application of deep learning to medical image segmentation. The system analyzes 3D medical scans and identifies anatomical structures using a U-Net convolutional neural network architecture.
The project combined machine learning with medical imaging libraries and a graphical interface for loading scans, processing image data, performing segmentation, and visualizing results.
The project contributed to our team earning 1st Place at the 2026 CEC Research Symposium at Georgia Southern University.
The project gave me experience working collaboratively on a larger technical system involving machine learning, computer vision, research, medical imaging, and user-facing software.