Luca was born and raised in Germany and completed a DIAB (German Abitur and high school diploma) at the German International School New York after moving to the United States. He is in the last year of his undergraduate degree with a Major in Physics and a Minor in Computer Science at McGill University. Luca joined Enger Lab in the first year of his undergraduate studies in 2018 and has been working on deep learning-based medical image analysis and treatment planning optimization.
Luca is part of the Enger Lab Artificial Intelligence group.
Multimodal Treatment Outcome Prediciton
Luca is developing a deep learning-based, multi-modal treatment outcome prediction model for endorectal cancer patients. The project combines radiomics with deep learning to increase prediction accuracy, increase efficiency, and eliminate human error to enable patient-specific treatment selection.
Tumor segmentation in endoscopy images
Luca is investigating the inter-observer variability of the manual segmentation of tumor regions in endoscopy images and its effect on treatment outcomes. Furthermore, Luca is developing a deep learning-based segmentation tool that can learn from multiple observers labels with high inter-observer variability.
McMedHacks – Medical Image Analysis and Deep Learning in Python
Luca is the co-director and founder of McMedHacks, which is an 8-week educational program about medical image analysis and deep learning in Python. McMedHacks consists of presentations from leaders in industry and academia, workshops from researchers in deep learning-based medical image analysis, and a hackathon. McMedHacks 2021 has 365 registered participants from 38 countries ranging from undergraduates to PhDs and MDs. The growing McMedHacks team consists of students from Enger Lab as well as collaborators from around the world with more than 30 members.