Teaching at Graduate Master Program of Medical Informatics

Second iteration of my teaching module within the Graduate Diploma in Medical Informatics, the joint program between Universidad de Chile and Heidelberg University coordinated through the Heidelberg Center for Latin America. As in 2012, the course was hosted at the Department of Anatomical Pathology, Faculty of Medicine, and I delivered it while working as a Research Engineer at SCIAN-Lab/BNI.

What changed from 2012

Having taught the module two years earlier, this second iteration benefited from concrete lessons learned. The 2012 experience revealed that health-sciences students often struggled with the jump from conceptual understanding to hands-on software use, while engineering students sometimes underestimated the biological context needed to interpret results correctly. In 2014 I restructured the sessions to address both gaps:

• More scaffolded practicals: step-by-step guided exercises were introduced before the open-ended analysis tasks, reducing the initial friction for non-technical students
• Biological context first: each technical topic now opened with a concrete biomedical question (e.g., “Are these two proteins interacting in the same cellular compartment?”) before presenting the mathematical formalism
• Improved example datasets: curated multi-channel fluorescence image stacks with known ground-truth co-localization, making it easier for students to verify their analysis pipelines

Topics

The core curriculum remained consistent with the 2012 module but was delivered with greater depth and confidence:

• Optical foundations of fluorescence microscopy and resolution limits
• Point Spread Functions (PSF): theoretical models and empirical measurement
• Deconvolution methods: Richardson-Lucy, Wiener filtering, and practical guidelines
• Spatial localization and sub-pixel fitting techniques
• Co-localization analysis: Pearson’s and Manders’ coefficients, intensity-based and object-based methods, statistical considerations

Hands-on practicals

Practical exercises were refined to include clearer assessment rubrics and intermediate checkpoints. Students worked through complete analysis workflows — from raw image stacks through preprocessing, deconvolution, and quantitative co-localization — using real microscopy data from SCIAN-Lab research projects. The interdisciplinary classroom setting (health and engineering students together) continued to be both the main challenge and the main strength of the course.

Software

1. FIJI
2. ImageJ
3. MATLAB