Additive manufacturing (AM) offers unique possibilities in comparison to conventional manufacturing processes. For example, complex parts can be manufactured without tools. For metals, the most commonly used AM process is laser-powder bed fusion (L-PBF). The L-PBF process is prone to process disturbances, hence maintaining a consistent part quality remains an important subject within current research. An established indicator for quantifying process changes is the dimension of melt pools, which depends on the energy input and the cooling conditions. The melt pool geometry is normally measured manually in cross sections of solidified welding seams. This paper introduces a new approach for the automated visual measuring of melt pools in cross-sections of parts manufactured by L-PBF. The melt pools are first segmented in the images and are then measured. Since the melt pools have a heterogeneous appearance, segmentation with common digital image processing is difficult, deep learning was applied in this project. With the presented approach, the melt pools can be measured over the whole cross section of the specimen. Furthermore, remelted melt pools, which are only partly visible, are evaluated. With this automated approach, a high number of melt pools in each cross-section can be measured, which allows the examination of trends over the build direction in a specimen and results in better statistics. Furthermore, deviations in the energy input can be estimated via the measured melt pool dimensions.