| Date: | Friday, Jun. 27 |
|---|---|
| Time: | 14:45 |
| Location: | N10_302, Institute of Computer Science |
Our guest speaker is Timo Blattner. He will present his Master Thesis.
You are all cordially invited to the CVG Seminar on June 27th, 2025 at 2:45 pm CEST
Recently, it has been shown that all mammal brains fold in a similar fashion, following the same mechanical model of folding. However, cetaceans remain outliers, having a systematically more folded brain than expected. A current hypothesis suggests that this is due to the increase in ambient pressure on the brain when these species dive, but this remains to be shown. Reconstructing these cortical surfaces is extremely difficult due to their high degree of folding and has never been done accurately before. We present a novel cortical surface reconstruction method, based on a few-shot learning of 2D expert manual tracings in each scan, to segment the full 3D image. From the segmentation, we reconstruct the white matter surface and displace it to the pial surface using a diffeomorphism. We successfully reconstruct the brains of 3 non-cetacean and 4 cetacean brains. We investigate the number of labeled slices needed for training a model to accurately reconstruct the cortical surface, and benchmark our method in humans. We show that these models can be used to label unseen scans of anatomically similar species, eliminating the need for manual labor. Our measurements support the validity of this pressure hypothesis.
Timo Blattner is a Master's student in Computer Science at the University of Bern. During his studies, he worked part-time as a research assistant in the Neuroradiology Department at the University Hospital of Bern, where he focused on deep learning-based segmentation and neuro-morphometric measurements aimed at improving clinical diagnostics. His research sparked international collaborations with partners in the UK and Brazil, allowing him to broaden his knowledge from clinical applications to the wider field of comparative neuroscience and the foundational scaling of brain morphology.