Topic: segmentation

Develop a complete CT-imaging workflow from scanner selection and acquisition settings to publication-quality visualisation. The course explains volumetric reconstruction, preprocessing and segmentation, including advanced and AI-assisted methods. Practical work improves segmented three-dimensional meshes, reduces noise and prepares datasets for quantitative analysis, images and video. It provides a technical foundation for beginners working with biological or earth-science specimens. Live sessions: 14, 16, 19, 21 and 23 October, 14:00–18:30, Europe/Madrid (CEST, UTC+02:00). Total 25 hours, i

Quantify biological shape through a reproducible three-dimensional morphometrics workflow in 3D Slicer and R. Participants connect image acquisition and resolution to segmentation and cleaning, collect landmarks and semilandmarks, and derive shape variables using Procrustes alignment and sliding procedures. Statistical exercises use principal components and regression to interpret size and shape variation; visualisations include deformations, morphings and heatmaps. Live sessions: 9–13 November, 13:00–18:00 daily, Europe/Madrid (CET, UTC+01:00). Total 30 hours: 25 live plus five independent.

Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural Networks (ffCNNs), inspired by this classic framework, have revolutionized computer vision and been adopted as tools in neuroscience. However, despite these successes, there is much more to vision. I will present our work using visual crowding and related psychophysical effects as probes into visual processes that go beyond the classic fra