Sensitivity and specificity of conventional cytological methods for cancer diagnosis can be raised significantly by applying further adjuvant cytological methods. To this end, the pathologist marks regions of interest (ROI) with a felt tip pen on the microscope slide for further analysis. This paper presents algorithms for the automated detection of these ROIs, which enables further automated processing of these regions by digital pathology solutions and image analysis. For this purpose, an overview scan is obtained at low magnification. Slides from different manufacturers need to be treated, as they might contain certain regions which need to be excluded from the analysis. Therefore the slide type is identified first. Subsequently, the felt tip marks are detected automatically, and gaps appearing in the case of ROIs which have been drawn incompletely are closed. Based on the marker detection, the ROIs are obtained. The algorithms have been optimized on a training set of 82 manually annotated images. On the test set, the slide types of all but one out of 81 slides were identified correctly. A sensitivity of 98.31% and a positive predictive value of 97.48% were reached for the detection of ROIs. In combination with a slide loader or a whole slide imaging scanner as well as automated image analysis, this enables fully automated batch processing of slides.