The Challenge: Identifying specific structures in medical images like MRIs or CTs is vital for diagnosis, treatment planning, and research. While AI has revolutionized this field by enabling high throughput, automated segmentation, developing these segmentation models typically requires significant programming skills, creating a barrier for many domain experts.
The Solution: Teramisu (Toolkit for Robust Medical Image Segmentation with U-Net) is a standalone desktop application that breaks down these barriers. It provides a seamless, user-friendly graphical interface that guides users through the entire lifecycle of a segmentation project: from raw image data to trained AI models, rigorous testing, insightful performance analysis, and compelling 3D visualizations. Teramisu demystifies complex deep learning workflows, making advanced image analysis accessible and efficient for everyone, regardless of coding background.
Its integrated, no-code approach offers a unique, all-in-one solution, significantly accelerating research and development in medical imaging.