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. 2020 Jul 24;237(6):1177–1184. doi: 10.1111/joa.13278

Producing 3D printed high‐fidelity retroperitoneal models from in vivo patient data: The Oxford Method

Matthew A Williams 1,✉, Robert W Smillie 1, Michael Richard 2, Thomas D A Cosker 1
PMCID: PMC7704231  PMID: 32706924

Abstract

Macroscopic anatomy has traditionally been taught using cadaveric material, lectures and a variable amount of additional resources such as online modules. Anatomical models have also been used to assist in teaching. Of these, traditional plastic models have been shown to be effective educational tools, yet have significant drawbacks such as a lack of anatomical detail and texturisation. Three‐dimensional (3D) printed models stand to solve these problems and widen access to high‐quality anatomical teaching. This paper outlines the use of 3D multi‐planar imaging (CT and MRI) as a framework to develop an accurate model of the retroperitoneum. CT and MRI scans were used to construct a virtual 3D model of the retroperitoneum. This was printed locally as a full‐size colour model for use in medical education. We give a complete account of the processes and software used. This study is amongst the first of a series in which we will document the newly formed Oxford Library of Anatomy. This series will provide the methodology for the production of models from CT and MRI scans, and the Oxford Library of Anatomy will provide a complete series of some of the most complex anatomical areas and ones which degrade quickly when a real cadaver is being used. In our own internal experience, the models are highly accurate, reproducible and durable, as compared to prosected specimens. We hope they will form an important adjunct in the teaching of the subject.

Keywords: anatomy, printing, three‐dimensional, retroperitoneum, urinary system

We outline the steps required to produce a high fidelity anatomical model from in vivo patient data, followed by a discussion of the advantages and disadvantages relative to current available options.

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1. INTRODUCTION

Classical methods of cadaveric dissection and/or prosection have not changed in over 400 years (Azer and Eizenberg, 2007) and remain the undisputed gold standard for the teaching of anatomy to undergraduate and postgraduate students (Korf et al., 2008; Balta et al., 2017). In recent times, issues have been raised regarding cost and access to bodies (Estai and Bunt, 2016) particularly with the new and added pressure of COVID‐19. Recent technological advances have only added to the pressure placed on medical schools to transition away from the use of cadavers (Howe et al., 2004; Ghazanfar et al., 2018). Despite this, it is still widely accepted that cadaveric methods should be maintained and recent advances used as adjuncts rather than replacements to cadaveric learning (Memon, 2018). It is well recognised that students learn best when multiple pedagogical approaches are used to complement one another (Estai and Bunt, 2016). Examples of such adjuncts are numerous and can largely be split into non‐digital and digital. Plastinated specimens from Gunther von Hagens (von Hagens, 1979) and plastic models represent the bulk of the former, whilst three‐dimensional imaging techniques including virtual (VR) and augmented reality (AR) represent examples of the latter (Tam, 2010; Moro et al., 2017; Tomlinson et al., 2019).

Plastic models are prevalent in medical education and have repeatedly been shown to be effective for the teaching of anatomy (Preece et al., 2013; Pawlina and Drake, 2013; Lombardi et al., 2014; Yammine and Violato, 2016). They can help mitigate the work of maintaining large libraries of physical cadaveric models and the logistical obstacles they pose; however, costs are often high, anatomical variation non‐existent, and anatomical accuracy only as good as the stylised and, often, over simplified representation produced by the artist (John et al., 2015).

In line with studies on plastic models, an emerging body of evidence points to the utility of 3D printed models in the teaching of anatomy. Studies show that 3D printed models are just as efficacious as, and in some cases better than, classic cadaveric or book‐based teaching (Lim et al., 2016; Chen et al., 2017; Garas et al., 2018; Guliev and Komyakov, 2019; Tanner et al., 2020). In addition, 3D printing presents solutions to many of the aforementioned issues regarding plastic models, especially if production can be done ‘in‐house’; costs are lower, and numerous models of the same system can depict differing anatomical variations with infinite possibilities. Furthermore, for complex anatomy it is feasible to print students an individual copy for future self‐directed study (Backhouse et al., 2019).

Recent advances in 3D printing have meant that costs of hardware have fallen significantly and the selection of colours and materials has increased (Smith and Jones, 2018). At the same time, open‐source software for designing models and in some cases even print‐ready detailed open‐source models have become available (Ganry et al., 2018). It is anticipated that the uptake of this technology by anatomy departments across the globe will increase in the coming years.

We therefore decided to develop our own 3D model printing algorithm: ‘The Oxford Method’, to supplement our provisions of cadaveric anatomy teaching. To test our process, we developed a model of the retroperitoneum for undergraduate medical students, with two main aims:

  1. To recreate the relational anatomy of the lumbar spine, urinary system and great vessels within the retroperitoneum.

  2. Visualise, in particular, the passage and blood supply of the ureters.

We propose that this, and similar models, can contribute to the education of undergraduate medical students at a moderate cost, especially in comparison to the cost of cadaveric provisions (O'Callaghan et al., 2017). We outline the steps required to produce this prototype, followed by a discussion of its advantages relative to current available options. Finally, we consider the limitations and future directions of 3D printing in this field.

The aim of this project was to develop a high‐fidelity custom anatomical model that would be of use for students at all levels of training: from very junior medical students with little direct experience of the relevant anatomy, to those in surgical training with greater levels of experience but who would still benefit from an anatomically accurate model from which to revise and study.

2. Methods

This retroperitoneal model was created from real patient CT and MRI data through a partnership between the University of Oxford and 3D LifePrints. 3D LifePrints is a technology company with a particular interest in applications of 3D printing in medicine. Our model comprises the bony anatomy of the abdomen and pelvis, and the major retroperitoneal vascular structures and organs, including the following:

  1. Lumbar spine, sacrum, coccyx, ilium, ischium and pubis

  2. Iliacus and psoas major

  3. The abdominal aorta (including major branches: adrenal arteries, coeliac axis, superior mesenteric artery, renal arteries, gonadal arteries, inferior mesenteric artery, coccygeal artery and common iliac arteries) and inferior vena cava (including major tributaries from: common iliac veins, renal veins and gonadal veins)

  4. Adrenal glands

  5. Kidneys, ureters and bladder

  6. Uterus, fallopian tubes and ovaries

The project workflow is shown in Figure 1 and described below.

Figure 1.

Figure 1

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Project workflow.

2.1. Imaging

To provide true anatomical representation, our model was re‐created from high‐resolution CT (100 kV, 275 mA) KUB (kidney, ureters and bladder) and MRI pelvis sequences in the Digital Imaging and Communication in Medicine (DICOM) format. Images were anonymised to preserve patient confidentiality. To preserve anatomical detail, the slice thickness of our scans was 0.625 mm. As part of this, we performed a CT angiogram to allow appreciation of the arrangement of the great vessels. Cross‐sectional image stacks were visualised in InSight.

2.2. Segmentation of anatomical structures

Anonymised DICOM data were uploaded to Simpleware ScanIP (www.simpleware.com/software/scanip/) which semi‐automatically classifies anatomical structures into bone, soft tissue and vascular structures using Hounsfield Units (HU) to determine tissue density (Figure 2). This allows for differing anatomical structures to be segmented into separate 3D objects which ultimately allows the whole model to be printed with differing anatomical structures in different colours and/or materials. Tissue classification happens in a slice‐by‐slice manner and so, due to the to the absence of imaging data for the space between slices, forms a scaffold that defines the outline of the anatomical structure and so defines the objects geometry. Formation of the 3D objects requires the interpolation of a scaffold into a 3D mesh that takes the shape of the selected tissue type.

Figure 2.

Figure 2

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Segmentation process in Simpleware ScanIP.

Due to the low contrast gradients between some abdominal anatomical structures on CT imaging, semi‐automatic segmentation was not possible for some anatomical structures. Manual segmentation may have taken a number of weeks (Garcia et al., 2018), and so it was necessary to recruit an anatomical artist to complete the model.

Figure 3 shows the collated 3D models representing the anatomical structures that were segmented on Simpleware ScanIP from both the CT and MRI DICOM datasets. Where appropriate, these 3D meshes were digitally optimised using Meshmixer® (version 3.5, http://www.meshmixer.com/). Meshmixer allows for meshes exported from Simpleware ScanIP to be repaired, modified and smoothed. This largely consisted of removing surface irregularities and sharp edges that presented as artefacts from the creation of the mesh. This is an important step in improving the resolution and contours of the model.

Figure 3.

Figure 3

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The 3D mesh representing the anatomical structures that were segmented on Simpleware ScanIP.

2.3. Computer‐aided design

The modified 3D mesh that was exported from Meshmixer was re‐designed in Blender (version 2.82, https://www.blender.org/ ) by an expert anatomy artist under the guidance of the Director of Anatomy (Figure 4). Blender was used to ‘create’ the anatomy that was not extracted from the CT or MRI sequences in Simpleware ScanIP. This included the inferior vena cava and its major tributaries, the renal arteries and veins, segmental blood supply to the ureters, the gonadal arteries and veins, the adrenal glands and their blood supply, musculature including psoas major, iliacus and the pelvic floor, and the sigmoid colon and rectum. It was hoped that the additional screening by the Director of Anatomy would prevent the introduction of any misconceptions regarding standardised anatomy. An example of this can be seen in Figure 4 where the annotations correctly point out that the suprarenal artery was of an incorrect calibre in the artist's initial rendering. Any uncertainties were correlated directly with the initial clinical imaging but also our repository of cadaveric specimens. Blender also allowed for the allocation of colours to match the anatomical structures.

Figure 4.

Figure 4

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Review by the University of Oxford Emeritus Professor of Anatomy.

The re‐designed 3D meshes were then exported out of Blender and back into Meshmixer. Minor positional changes were made to ensure all component pieces were aligned correctly, thus forming the digital prototype (see Figures 5 and 6) of many objects that would be printed together to form a 3D model. This alignment is critical as it avoids the overlapping of objects which causes printing errors. Meshmixer was also used to create a stand for the model. Additional modifications could then be made by the artist, including the creation of a ‘window’ in the anterior bladder wall to permit visualisation of internal structures such as the ureteric and internal urethral orifices, and the trigone. (see Figure 7).

Figure 5.

Figure 5

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Anterior view of the digital prototype.

Figure 6.

Figure 6

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Posterior view of the digital prototype.

Figure 7.

Figure 7

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Final model design for printing as viewed in GrabCAD.

Once the digital 3D model was completed, the file was converted to the Standard Tessellation Language file format (.STL) and uploaded to the 3D printer software GrabCAD (https://grabcad.com/) for production.

2.4. Material selection

The colour range of the model was determined by the material selection, which was determined by the 3D printing hardware available to 3D LifePrints. The printer was a connex3 object 260 which allows for three polymers to be simultaneously 3D printed. The materials chosen were VeroMagenta, VeroYellow and VeroWhite which can be mixed to give a colour palette.

2.5. Printing process

GrabCAD calculates the most economical print orientation whilst considering the need to support overhanging 3D parts of the model with a removable support polymer (SUP 706B support). After this calculation, the printing process took 58 hours and the model was printed in two halves. The model used 11.36 kg of plastic material, comprising 4.7 kg of VeroWhite, 0.4 kg of VeroMagenta and 0.16 kg of VeroYellow. The removable support required 6.1 kg of SUP 706B. The cost of the raw plastic materials alone was £2,223.02. After printing was complete, post‐processing involved use of a chemical solution to remove the support polymer and joining of the separate sections (Figure 8). For added stability and better surface finish, the model was then spray coated with 5 coats of acrylic matt gloss paint, after which it was mounted in a case (Figure 9).

Figure 8.

Figure 8

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Post‐processing.

Figure 9.

Figure 9

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Final retroperitoneal model.

3. DISCUSSION

We have described the design and production process of a high‐fidelity 3D printed model generated from in vivo patient data of the retroperitoneum for the purposes of augmenting traditional anatomy education methods. Similar models are being developed across another 20 bodily regions including the brachial plexus, hepatobiliary system and base of skull, so that ultimately the Oxford Library will represent a comprehensive virtual library of some of the most complex anatomical structures.

3.1. Flexibility of design

A key benefit of 3D printing is the flexibility it allows in the design and production of models. Pre‐made plastic models typically come in a one‐size‐fits‐all form, whereas 3D printing allows educators to personalise the model according to the level of education of the students. A crucial distinction here would be undergraduate and postgraduates, the latter often being surgical trainees who require a more intimate knowledge of anatomy and a knowledge of key surgical landmarks.

For those needing anatomical knowledge to perform clinical procedures, there is no substitute for knowing the anatomical changes that precede the need for the procedure. CT imaging of patients with pathology would enable the production and use of 3D printed models of the pathological anatomy (e.g., abdominal aortic aneurysms) in training (Dhir et al., 2015). Similarly, there is no substitute for exposure to anatomical variation for trainee surgeons. The ability to print real‐life anatomical variations will benefit surgical trainees as it prepares them for the jump from theoretical anatomical learning to the practical application of their knowledge in theatres.

3.2. Production of this model

The infrastructure to design and print this model was well established. However, any groups intending to start out in this field should first consider the logistics and costs of establishing this technology. The most basic 3D printer can be purchased relatively cheaply for a price in the region of £250 ‐ £500. More advanced set‐ups, utilised in this instance, that include specialist software licences, in‐house developers and state of the art printers, can easily be assumed to amount to a price well over a thousand‐fold greater.

The development of this model took considerable time and skill from all those involved. Discounting these design and development costs, the costs involved in running the 3D printer, maintaining the printer and the costs of running the business the printer were owned by (as they will be highly variable depending on local infrastructure), the one constant is the cost of the plastic materials – which for this model was £2,223.02. In an established 3D printing centre or one with access to ready‐made designs, this is a cost that is significantly less than the sum of plastic models of the same anatomy. For example at current prices, and from a well‐known manufacturer of such models, excluding postage and value added tax (VAT), a plastic model of the female pelvis costs £582, a model of the urinary tracts costs £785, a model of the kidney costs £275, and a model of the posterior abdominal wall costs £1,957 (prices as of the 06/04/2020). It is worth noting that our 3D printed model is a true to size model, and so any scale model would be both cheaper and faster to print; a half size model would use 1/8th the volume of plastic.

Whilst the cost of 3D printing to the standard required for teaching anatomy (as opposed to providing spatial perspective to surgeons planning operations) as described in the study is not an instant game changer, the technique does reduce ethical and safety concerns associated with cadaveric specimens. The modern 3D printer can be housed in an office and printing comes with few, if any, ethical or safety concerns. By comparison, sourcing cadavers has become ever more difficult (Gangata et al., 2010; Habicht et al., 2018). Further, the storage of cadavers is complex, and wear and tear is to be expected, so a team of skilled prosectors must be employed to maintain the supply for teaching.

3.3. Issues with production

The semi‐automatic segmentation method described above is helpful, yet for those anatomical tissues between which there is low contrast it has its limitations. As such, a number of structures were drawn in following co‐ordination between our senior anatomists, a software engineer and sometimes a medical artist. We believe this goes some way to explaining why many of the 3D models that have been published relate to areas with stark distinction between tissues: blood vessels can utilise angiography, cardiac structures are easily seen on MRI, and the bone is well demarcated from soft tissue (Martelli et al., 2016; Garcia et al., 2018).

It would be remiss to suggest the uptake of 3D printing without some consideration to the environment. Worldwide and across many disciplines, 3D printing is likely to reduce CO2 emissions and lead to a more sustainable approach to manufacturing (Gebler, 2015). We do not envisage 3D modelling replacing cadaveric anatomy but instead comprising an additional teaching modality, and therefore, it may be that 3D printing offers an alternative, more environmentally friendly approach, with a smaller burden on our carbon footprint than conventional manufacturing processes. Either way, educators should be aware of the environmental and social costs of 3D printing (Behm et al., 2018).

3.4. Limitations with this model

As seen in the Figures 3, 4, 5, this model, designed for the teaching of undergraduates, does not have the same level of detail as is found in the human body. This same issue is found in other plastic models when compared to plastinated specimens of preserved cadavers (Balta et al., 2017; Mogali et al., 2018). Despite the level of detail, this retroperitoneal model has good anatomical fidelity. However, the same cannot be said about its ability to model the tissue itself. Despite using state of the art printing methods and combining materials of differing structure, textures and colours, this model would not be suitable for surgical training requiring dissection, cutting and suturing, and does not give students an appreciation of the texture and pliability of human tissue. For this reason, a valid recommendation may be that 3D printed models be utilised in the teaching of novices and that cadaveric specimens be reserved for more experienced students demonstrating greater ‘upfront’ knowledge and curiosity about anatomy.

Constructing models upon which practical training can be done has been well received in endoscopic training (Waran et al., 2015) and neurosurgery (Waran et al., 2014) and should be a target for future developments to this model. Bioprinting is an emerging field that seeks to 3D print whole functioning organs (Tappa and Jammalamadaka, 2018). Whilst this would clearly be beyond the function necessary to teach anatomy at any level, a middle ground of material found between this dream and our current reality would seem perfect.

3.5. Future perspective

This study is amongst the first of a series in which we will document the newly formed Oxford Library of Anatomy. When completed, this will be a repository containing the entire range of human anatomy as more 3D models become available for printing. As barriers to entry regarding in‐house 3D printing continue to fall, it is hoped that medical students and schools around the world with access to 3D printing technology will be able to download and print required models. We hope to develop a new generation of 3D models that incorporate haptic technology such that a student will be presented with additional virtual information (audio visually) about different parts of the model as they lay their hands on them.

4. CONCLUSION

3D printed models represent an exciting new type of learning resource available for use in anatomy education. We have presented The Oxford Method of producing high‐fidelity anatomical models as exemplified through our version of the retroperitoneum generated from in vivo patient data.

The utility of converting complex medical imaging into real‐world models is an exciting prospect for undergraduate anatomical education. We hope we and others can replicate this workflow to produce many more customised teaching models to augment traditional cadaveric teaching methods. With a reliable and established production process, we are now in a position to begin formally appraising the efficacy of 3D printing in medical education.

Conflict of interest

None.

Author Contributions

MAW: concept/design, acquisition of data, data analysis/interpretation, drafting of the manuscript, critical revision of the manuscript and submission of the article. RWS: concept/design, acquisition of data, data analysis/interpretation and drafting of the manuscript. MR: concept/design, acquisition of data, data analysis/interpretation and drafting of the manuscript. TC: concept/design, critical revision of the manuscript and approval of the article.

Williams MA, Smillie RW, Richard M, Cosker TDA. Producing 3D printed high‐fidelity retroperitoneal models from in vivo patient data: The Oxford Method. J. Anat. 2020;237:1177–1184. 10.1111/joa.13278

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