**1. Introduction**

Within recent years there has been exponential growth in healthcare related 3D printing research (as shown in **Figure 1**). This growth is translating into clinical practice as accessibility to 3D printers increases. One of the drivers for the growth 3D printing within healthcare is a trend towards development of 'personalised' medicine. Personalised medicine is "a move away from a 'one size fits all' approach to the treatment and care of patients with a particular condition, to one which uses new approaches to better manage patients' health and target therapies to achieve the best outcomes" [1]. 3D printing has been shown to be useful for: patient education [2–4] education for healthcare professionals [5], procedure planning [6, 7] and prosthesis / implant production [8] and is set to be promising in the areas of regenerative medicine and tissue engineering. Before we describe each above-mentioned section, we will highlight the workflow from medical images acquisition to application (see **Figure 2**).

**2. Workflow**

for 3D printing.

**2.1. Imaging**

**2.2. Segmentation**

The use of products derived from 3D mesh models and computer aided design (CAD) techniques in healthcare is rapidly growing. Applications include: planning surgical procedures for hepatic & renal cancer resection; innovative cardiac and vascular device testing for paediatric and adult populations; visualisation of complex head and neck anatomy for neurosurgeons; practicing procedures ex vivo; training models and educating clinicians and patients [9–13]. Models of heart [2], renal collecting system [14], kidney [15] and brain [16, 17] have been previously developed. Model production requires knowledge of how to segment the region of interest from medical image data, manipulate the resulting 3D model and prepare stereolithographic (.stl) files

Patient-Specific 3D Printed Models for Education, Research and Surgical Simulation

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In this section we present a pipeline that converts medical images of body structures to 3D print models. Particularly, we discuss how to load and manipulate 3D medical image data, use simple processing tools to extract volumes and structures from the images, export those volumes into 3D printing software where they can refine and repair their models. We demonstrate our streamlined processing pipeline on 3D printed model of a lung, which was fabricated using filament deposition-modelling additive printing technique. This model was

This section will be of interest to students and professionals from medical biomedical and engineering backgrounds that wish to learn basic image processing and volume extraction techniques. The materials will make it possible to develop 3D models from medical images, which can be used as a learning aid to help visualise anatomy. As shown in **Figure 2** the process starts with a 3D medical image, from which a structure will be extracted. The particular

The nature of the imaging data depends on the specific imaging technology and the region of interest being imaged (see **Table 1**). Image resolution can vary between 0.1 and 8 mm, while image intensity can be due to density, light absorption of acoustic impedance. The main medical data file types are DICOM, NIFTI and MINC. DICOM is a universal image format and file sharing protocol, suitable for multiple image modalities and very widely used. It is easy to import into most software. NIFTI is a format designed specifically to store neuroimaging data. This format is compatible/viewable with several specific software packages. MINC is a

The next stage of the image-processing pipeline is segmentation, which refers to the extraction of a specific 3D volume from a set of image data/slices. It is used to locate objects and boundaries in each slice that corresponds to the tissue of interest. As it is done slice by slice, a volumetric data is gradually built up. It can be used to create patient specific, highly accurate models of organs, tissue and pathology. Many software packages are available [10, 18], here we mention only Slicer. The volume can be extracted using basic or advanced segmentation techniques.

segmented from medical data using the freely available segmentation software Slicer.

nature of the image will inform how it is processed.

format used with certain brain imaging software.

**Figure 1.** Chart demonstrating the number of citations in PubMed (https://www.ncbi.nlm.nih.gov/pubmed/) per annum from 2000 to 2017 in PubMed using the search terms '3D printing', '3D printing surgery', '3D printing medicine', '3D printing radiology'.

**Figure 2.** Outline of the workflow from medical images acquisition to application of 3D printed models.
