Prior to radiation therapy planning, tumours and organs at risk need to be delineated. In recent years, deep learning models have opened the possibility of automating the contouring process, speeding up the procedures and helping clinicians. However, deep learning models, trained using ground truth labels from different clinicians, inevitably incorporate the human-based inter-observer variability as well as other machine-based uncertainties and biases. Consequently, this affects the accuracy of segmentation, representing the primary source of error in contouring tasks. Therefore, clinicians still need to check and manually correct the segmentation and still do not have a measure of reliability. To tackle these issues, researchers have shifted their focus to the topic of probabilistic neural networks and uncertainties in deep learning models. Hence, the main research question of the project is whether a 3D U-Net neural network trained on CT lung cancer images can enhance clinical contouring practice by implementing a probabilistic auto-contouring system. The Monte Carlo dropout technique was employed to generate probabilistic and uncertainty maps. The model calibration was assessed using reliability diagrams, and subsequently, a clinical experiment with a radiation oncologist was conducted. To assess the clinical validity of the uncertainty maps two novel metrics were identified, namely mean uncertainty (MU) and relative uncertainty volume (RUV). The results of this study demonstrated that probability and uncertainty mapping effectively identify cases of under or over-contouring. Although the reliability analysis indicated that the model tends to be overconfident, the outcomes from the clinical experiment showed a strong correlation between the model results and the clinician’s opinion. The two metrics exhibited promising potential as indicators for clinicians to determine whether correction of the predictions is necessary. Hence, probabilistic models revealed to be valuable in clinical practice, supporting clinicians in their contouring and potentially reducing clinical errors.

The last decade has seen great advances within the field of artificial intelligence. One of the most noteworthy areas is that of deep learning, which is nowadays used in everything from self driving cars to automated cancer screening. During the same time, the amount of spatial data encompassing not only two but three dimensions has also grown and whole cities and countries are being scanned. Combining these two technological advances enables the creation of detailed maps with a multitude of applications, civilian as well as military.This thesis aims at combining two data sources covering most of Sweden; laser data from LiDAR scans and surface model from aerial images, with deep learning to create maps of the terrain. The target is to learn a simplified version of orienteering maps as these are created with high precision by experienced map makers, and are a representation of how easy or hard it would be to traverse a given area on foot. The performance on different types of terrain are measured and it is found that open land and larger bodies of water is identified at a high rate, while trails are hard to recognize.It is further researched how the different densities found in the source data affect the performance of the models, and found that some terrain types, trails for instance, benefit from higher density data, Other features of the terrain, like roads and buildings are predicted with higher accuracy by lower density data.Finally, the certainty of the predictions is discussed and visualised by measuring the average entropy of predictions in an area. These visualisations highlight that although the predictions are far from perfect, the models are more certain about their predictions when they are correct than when they are not.