Villum Experiment:
3D tomograms (volumetric images resulting from computed tomography) acquired at synchrotron or laboratory facilities provide a view into the internal
structure of matter. The need for accurately segmenting tomograms has not yet
been met by the deep learning (DL) methods, even though DL has successfully
solved most segmentation problems in computer vision. Contrary to an obvious
suggestion, I claim that successful DL methods for segmenting photographs
should not serve as inspiration for segmenting tomograms, as it will lead to large
and data-hungry models. Instead, I suggest developing models which exploit the
simplicity of tomograms.
The methods I intend to develop diverge from the current image segmentation
paradigm, and cannot lean on the existing frameworks for DL. This makes
method development difficult and time-consuming. But, if successful, the new
methods will pave the way for future research in DL for tomograms.