The fields of operations research and computer science have long sought to find automatic solver techniques that can find high-quality solutions to difficult real-world optimisation problems. The traditional workflow is to exactly model the problem and then enter this model into a general-purpose "black-box" solver. In practice, however, many problems cannot be solved completely automatically, but require a "human-in-the-loop" to iteratively refine the model and give hints to the solver. In this paper, we explore the parallels between this interactive optimisation workflow and the visual analytics sense-making loop. We assert that interactive optimisation is essentially a visual analytics task and propose a problem-solving loop analogous to the sense-making loop. We explore these ideas through an in-depth analysis of a use-case in prostate brachytherapy, an application where interactive optimisation may be able to provide significant assistance to practitioners in creating prostate cancer treatment plans customised to each patient's tumour characteristics. However, current brachytherapy treatment planning is usually a careful, mostly manual process involving multiple professionals. We developed a prototype interactive optimisation tool for brachytherapy that goes beyond current practice in supporting focal therapy - targeting tumour cells directly rather than simply seeking coverage of the whole prostate gland. We conducted semi-structured interviews, in two stages, with seven radiation oncology professionals in order to establish whether they would prefer to use interactive optimisation for treatment planning and whether such a tool could improve their trust in the novel focal therapy approach and in machine generated solutions to the problem.

3D visualisation is being used increasingly to improve the accuracy of biopsies and seed placement for prostate cancer brachytherapy (the implantation of small radioactive ‘seeds’ for radiotherapy). The construction of a 3D model requires the segmentation of 2D Magnetic Resonance (MR) images, which remains a challenging problem. The current practice of manual segmentation requires a very high level of expertise and is time-consuming. In this paper, we propose a new method for creating a 3D model of the prostate from MR images obtained pre-operatively. To do this, an Active Appearance Model (AAM) is created from the image data by grouping the MR images based on the region of the prostate each is taken from. A fast optimisation algorithm is then used to segment a new prostate using the AAM. Our results show that this method is fast and accurate compared with manual segmentation enabling this method to be used intra-operatively.