Organ Segmentation

In the course “Programming for Machine Learning and Image Processing in Medicine”, I developed a neural network for organ segmentation.

We worked in C++ with LibTorch. The project covered preprocessing, feature extraction, Random Forests, neural networks and noise reduction using a bilateral filter.

The implementation was followed by prototyping and integration with Python and Streamlit. The models differentiated organs, but precision stayed below expectations. A larger dataset would likely have improved performance substantially.

Technologies: C++, LibTorch, Python, Streamlit