Diabetic foot ulcers are a dangerous complication of diabetes and, if uncared for, can lead to significant health risks. In treatment, wound annotation and measurements are crucial. The development of AI models has the potential to significantly transform medical diagnostic techniques and greatly benefit the daily work of medical staff. With the support of deep learning, we aim to expand on new ways to implement convolutional neural networks (CNNs) in the daily work of medical staff. In this study we used the well renowned nnU-Net 2.0 architecture. The models are trained using custom loss function which improves segmentation accuracy compared to the state of the art approaches. Segmentation was performed using an ensemble of models. For the dataset, we obtained 451 wound pictures with ground truth segmentations of wounds and scale markers and split the dataset into a holdout test part of 15% images and 85% of training/validation images. Various models achieved accuracy of 90,91%, 90.88% and 91.65%. Model ended up with big accuracy and could be used in a medical setting.