TUM.ai Computer Vision Project

Computer Vision project for cell tracking and segmentation conducted under the supervision of Amirhossein Kardoost from Helmholtz Marr Lab as part of TUM.ai’s research track.

This project focuses on developing advanced computer vision techniques for biological applications, specifically targeting automated cell tracking and segmentation in microscopy data. The work involves applying state-of-the-art deep learning methods to solve challenging problems in computational biology.

Key Areas

Computer Vision & Image Segmentation: Developing robust algorithms for accurate cell boundary detection and tracking across time series data.

Research Collaboration: Working closely with researchers at the Helmholtz Institute to ensure practical applicability of developed methods.

Machine Learning: Implementing and fine-tuning deep learning models for biological image analysis.

This project represents an exciting intersection of artificial intelligence and biological research, contributing to advances in automated microscopy analysis that can accelerate biological discovery.