STUDIOS: Segmenting Tomograms Using Different Interpretation of Simplicity

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.