  In synchrotron-based Computed Tomography (CT) there is a trade-off between
spatial resolution, field of view and speed of positioning and alignment of
samples. The problem is even more prominent for high-throughput tomography--an
automated setup, capable of scanning large batches of samples without human
interaction. As a result, in many applications, only 20-30% of the
reconstructed volume contains the actual sample. Such data redundancy clutters
the storage and increases processing time. Hence, an automated sample
localization becomes an important practical problem. In this work, we describe
two self-supervised losses designed for biological CT. We further demonstrate
how to employ the uncertainty estimation for sample localization. This approach
shows the ability to localize a sample with less than 1.5\% relative error and
reduce the used storage by a factor of four. We also show that one of the
proposed losses works reasonably well as a pre-training task for the semantic
segmentation.
