Before understanding the social outreach and impact of this thesis’ research, it is important to have an understanding what exactly this project was about and how it came about. Of note here is that this thesis links to research within BioMMedA (Biophysical Models for Medical Applications) about transarterial therapies and 3D modelling of microsphere transport and distributions throughout the liver. The main goal of this thesis was to develop patient-specific 3D reconstructions of the hepatic arterial tree of patients with hepatocellular carcinoma (HCC), the most common kind of liver cancer worldwide. Firstly, a theoretical introduction to the main topics of interest was made, thereby explaining HCC and treatment options, after which the practical side of this thesis, namely the segmentation and 3D reconstructions of the structures, commenced. There were seven patients to segment, and all seven patients had at least one tumour lesion bigger than 1 cm. The models that needed to be created consisted of the arteries of the liver and the HCC tumours present. To start the segmentation process, Dyna-CT (Computed Tomography) scans were retrospectively obtained containing images with contrast injection, so that the arteries and contrast-captivating tumours are clearly visible. Using the software package Mimics (Materialise), the Dyna-CT scans can be uploaded into the application, where the segmentation process begins. All Dyna-CT scans are divided in 2 dimensional slices, and by using the functions available in Mimics, a 3-dimensional object can be generated. This process was the most time-consuming part of this thesis and was done for the arteries of the seven patients as well as for all tumour lesions with a maximal diameter bigger than 1 cm. This thesis comprises a detailed description of the process of segmentation, along with a discussion of its results and its limitations. Furthermore, the tumours that were found were characterised into a table, so that there is a clear overview of which lesion was found and what its features are. These aspects of characterisation are necessary for future research on HCC tumours and their therapies, as not only the tumours are objects of further research, but also the whole 3D structure, thereby including the arteries. This is because, after the 3D modelling of the structures, further research is possible by doing mathematical calculations and simulations of fluids (Computational Fluid Dynamics) so that the transarterial treatment options can be explored. In this thesis the challenges and nuances involved in the segmentation process of the arterial trees and tumours are highlighted so that the limitations and future directions of accurate modelling and patient-specific analysis are explored. The manual refinement and the quality of the imaging data, particularly contrast visibility, have a significant impact on the results. Other limitations such as variations in tumour size measurements and the need for standardisation in patient reporting are also explored in the prospect of further research. The social impact of this project will be felt when further research on these models is done, so that clinical implications of transarterial radio-and chemoembolization (TARE and TACE respectively) treatments are possible. When further advancing in this research project of BioMMedA, more data will be assembled which leads to a bigger patient-specific database of 3D models, with other options such as artificial intelligence that can use this database as a learning base to further advance in the 3D modelling. Because of this informed and data-driven approach, this thesis helps to set a foundation for the implementation of more personalised and effective treatment strategies for patients with HCC. The utilisation of 3D models and innovative treatment planning not only enhances treatment precision but also offers a more patient-centric approach. Fundamentally, on the longer term, this thesis has the promise of improving health outcomes, influencing and improving the lives of people affected by HCC.