Teaching at Graduate Master Program of Medical Informatics

This was my first formal teaching experience, undertaken while working as a Research Engineer at SCIAN-Lab (Scientific Image Analysis Laboratory), part of the Biomedical Neuroscience Institute (BNI) at the Faculty of Medicine, Universidad de Chile. The course was a module within the Graduate Diploma in Medical Informatics, a joint academic program between Universidad de Chile and Heidelberg University (Germany), coordinated through the Heidelberg Center for Latin America.

The core pedagogical challenge was interdisciplinarity: the classroom brought together graduate students from health sciences (physicians, medical technologists, biologists) and engineering backgrounds (electrical, biomedical, computer science) in the same sessions. This meant that every lecture and practical had to bridge two fundamentally different ways of thinking — clinicians who needed intuitive understanding of what the tools do, and engineers who wanted the mathematical formalism behind them.

Topics

Lectures and hands-on sessions covered the physical and mathematical foundations of fluorescence microscopy image analysis:

• Optical foundations: image formation in fluorescence microscopy, diffraction limits, and the concept of resolution
• Point Spread Functions (PSF): theoretical models, empirical measurement, and their role as the fundamental descriptor of an imaging system’s behavior
• Deconvolution: inverse problems in microscopy, Richardson-Lucy and Wiener filter approaches, practical guidelines for when deconvolution helps and when it introduces artifacts
• Spatial localization: sub-pixel localization of fluorescent emitters, Gaussian fitting, and connections to super-resolution principles
• Co-localization analysis: Pearson’s and Manders’ coefficients, intensity-based vs. object-based approaches, and the statistical pitfalls of co-localization metrics (e.g., sensitivity to background, thresholding effects)

Hands-on practicals

The practical sessions were essential, particularly for students from non-technical backgrounds who needed to develop confidence manipulating digital images and interpreting quantitative outputs. Exercises included PSF visualization, deconvolution of real microscopy stacks, and co-localization workflows on multi-channel fluorescence datasets. These sessions were designed so that every student, regardless of background, could complete them and interpret the results meaningfully.

Software

1. FIJI
2. ImageJ
3. MATLAB