  The topology and surface characteristics of lyophilisates significantly
impact the stability and reconstitutability of freeze-dried pharmaceuticals.
Consequently, visual quality control of the product is imperative. However,
this procedure is not only time-consuming and labor-intensive but also
expensive and prone to errors. In this paper, we present an approach for fully
automated, non-destructive inspection of freeze-dried pharmaceuticals,
leveraging robotics, computed tomography, and machine learning.
