Microscopes are used in biomedical sciences e.g. in the context of pathology. There, one task is for the pathologist to judge the severity of a resected tumor. Today, digital pictures from such microscopes are readily available and (in theory) open the possibility for automation by means of analyzing these data algorithmically. Yet, even though manual analysis of such images by human experts is time‐consuming, tedious, and suffers from inter‐operator variability, automated algorithms nevertheless do not yet reach the standards required for their routine clinical usage. One reason for this is the difficutly of a process called “segmentation”, which is one of the algorithmic steps comprising a potential automated, algorithmic approach. The term “segmentation” describes the identification of different objects in an image (e.g., tumor cells separated from other visible tissue in a pathological micrograph). There, the classical algorithmic approach is to identify “regions” in digital images which show objects (e.g., tumor cells). Even though a large variety of theoretically well‐founded approaches to this problem exist in the relevant literature, no solution seems to be sufficiently reliable, to date. In this work, we describe how in certain situations such as the computer‐aided grading of meningioma, the problem can be simplified, thus avoiding the difficult segmentation step. Instead, we describe and further automate a statistical method to find only the loci of tumor cells and (separately) how to employ a method described elsewhere to decide whether cells have potential to proliferate in terms of Ki‐67 immunohistochemistry. Based on a collection of micrographs from different mengingioma and basd on a public benchmark database, we are able to demonstrate the usefulness of our methods.