The additive manufacturing (AM) process plays an important role in enabling cross-disciplinary research in engineering and personalised medicine. Commercially available clinical tools currently utilised in radiotherapy are typically based on traditional manufacturing processes, often leading to non-conformal geometries, time-consuming manufacturing process and high costs. An emerging application explores the design and development of patient-specific clinical tools using AM to optimise treatment outcomes among cancer patients receiving radiation therapy. In this review, we: 1)? highlight the key advantages of AM in radiotherapy where rapid prototyping allows for patient-specific manufacture 2) explore common clinical workflows involving radiotherapy tools such as bolus, compensators, anthropomorphic phantoms, immobilisers, and brachytherapy moulds; 3) investigate how current AM processes are exploited by researchers to achieve patient tissuelike imaging and dose attenuations. Finally, significant AM research opportunities in this space are highlighted for their future advancements in radiotherapy for diagnostic and clinical research applications.

Magnetic resonance imaging (MRI) has been a prevalence technique for breast cancer diagnosis. Computer-aided detection and segmentation of lesions from MRIs plays a vital role for the MRIbased disease analysis. There are two main issues of the existing breast lesion segmentation techniques: requiring manual delineation of Regions of Interests (ROIs) as a step of initialization; and requiring a large amount of labeled images for model construction or parameter learning, while in real clinical or experimental settings, it is highly challenging to get su±cient labeled MRIs. To resolve these issues, this work proposes a semi-supervised method for breast tumor segmentation based on super voxel strategies. After image segmentation with advanced cluster techniques, we take a supervised learning step to classify the tumor and nontumor patches in order to automatically locate the tumor regions in an MRI. To obtain the optimal performance of tumor extraction, we take extensive experiments to learn parameters for tumor segmentation and classification, and design 225 classifiers corresponding to different parameter settings. We call the proposed method as Semi-supervised Tumor Segmentation (SSTS), and apply it to both mass and nonmass lesions. Experimental results show better performance of SSTS compared with five state-of-the-art methods.