In tropical and subtropical countries with bad infrastructure there exists diseases which are often neglected and untreated. Some of these diseases are caused by parasitic intestinal worms which most often affect children severely. The worms spread through parasite eggs in human stool that end up in arable soil and drinking water. Over one billion people are infected with these worms, but medication is available. The problem is the ineffective diagnostic method hindering the medication to be distributed effectively. In the process of designing an automated microscope for increased effectiveness the solution for marking out the stool sample on the microscope slide is important for decreasing the time of diagnosis. This study examined the active contour model and four different semantic segmentation networks for the purpose of delineating the stool sample from the other parts of the microscope slide. The Intersection-over-Union (IoU) measurement was used to measure the performance of the models. Both active contour and the networks increased the IoU compared to the current implementation. The best model was the FCN-32 network which is a fully convolutional network created for semantic segmentation tasks. This network had an IoU of 95.2%, a large increase compared to the current method which received an IoU of 77%. The FCN-32 network showed great potential of decreasing the scanning time while still keeping precision of the diagnosis.