Segmentation of a human brain dataset.

Project Type: Personal Demonstration.

Tools Used: 3DSlicer, Autodesk Fusion, PrusaSlicer (Prusa MK4S).

Skills: Segmentation Analysis, CAD Modelling, Medical Additive, Scientific Communication.

Overview: 

My concept was to produce an interactive, physical model of a brain and associated deep tumour from a sample dataset. The final product should be good enough to be handled by a patient. The final parts were donated to PrintCity MMU and the NHS Christie Hospital, to be used for training purposes, and were extremely well received.

Holding the Diagnosis: How 3D Printing Bridges the Gap in Cancer Communication.

One in two people will face a cancer diagnosis at some point in their lifetime. For many, this moment creates an immediate fracture, splitting their lives into a distinct before and after. While facing mortality is a primal human fear, modern medicine is making monumental strides to soften the blow of that initial flashpoint. Advanced screening catches pathologies earlier, pioneering treatment methodologies are entering clinical trials daily, and long-term survival rates continue to climb (Cancer Research UK 2026). Yet, in the sterile environment of a consultation room, these macro-level triumphs can be impossible for a newly diagnosed patient to digest. One critical, often overlooked step toward easing this cognitive burden is fundamentally reframing how a patient understands their own diagnosis (McNeil and Arena 2017).

This is where the intersection of healthcare and rapid, localised digital fabrication offers a paradigm shift. Moving beyond flat, abstract pixels on a monitor, I collaborated with clinical stakeholders to develop physical, proof-of-concept anatomical models of brain tumours derived directly from patient MRI datasets. By segmenting complex DICOM scans, it is possible to isolate a tumour volume and, within hours, translate it into a tangible, real-scale 3D media asset. Medical practitioners spend years training to naturally parse and comprehend three-dimensional pathology from a two-dimensional screen; the average patient simply cannot. All too often, what is meant to be an explanatory scan reads to a patient as an intimidating, confusing mass of grey shapes and clinical metrics. Providing an exact, physical model allows individuals to literally hold the object of their diagnosis. It replaces the isolating experience of nodding along in polite confusion with genuine, empathetic comprehension.

From Pixels into Polymers.

Traditional medical imaging is a modern marvel of data acquisition, yet it introduces a significant spatial deficit into the patient consultation. When a patient is presented with a complex MRI or CT scan, they are being asked to mentally reconstruct a series of flat, two-dimensional greyscale slices into a three-dimensional understanding of their own body. In a high-stakes environment like oncology, this cognitive leap is frequently obscured by panic and unfamiliar terminology. The result is a profound communication barrier at the exact moment when total clarity is required.

One way of overcoming this barrier is a fundamental shift in health literacy, one that leverages advanced digital workflows to better "democratise" clinical data. The process begins with medical image segmentation, utilising specialised software like 3D Slicer to isolate volumetric data from standard DICOM files. By systematically separating tumorous tissues from healthy anatomical structures voxel by voxel, we can translate abstract mathematical data into a precise, digital asset.

The real magic occurs when this digital asset is then bridged with localised additive manufacturing. What once lived exclusively behind a monitor is transformed into a high-fidelity anatomical model that a patient can hold, turn over, and truly comprehend. This is just about producing a visual aid but about reducing the cognitive load on the individual, fostering immediate understanding, and establishing a baseline for genuinely informed consent (Traynor et al. 2022).

Historically, the barrier to integrating tactile models into daily clinical practice has been time and scalability. However, as digital design platforms evolve and artificial intelligence-assisted automation streamlines the initial segmentation process, the timeline from initial scan to physical model is shrinking from days to hours. By automating the routine aspects of data processing, medical communicators and clinicians can focus entirely on what matters most: utilising these assets to drive deeper and more empathetic human connection at the bedside.

The Future of Integrated Health Communications.

As we look toward the future of medical communications, a possible mandate emerges: we must evolve alongside the technologies reshaping clinical care. Agencies and practitioners can no longer rely solely on the written word or flat graphics to convey life-altering medical realities. The integration of spatial data, rapid prototyping, and automated digital workflows may represent the next frontier in patient advocacy.

By taking data out of the screen and putting it directly into the hands of those who need it most, we change more than just a conversation in a consultation room. We dismantle the barriers of complex medical jargon, alleviate the acute anxiety of the unknown, and replace isolating confusion with deep, empathetic comprehension. True innovation in healthcare communication doesn’t just explain the science, but makes it tangible.

References.

Cancer Research UK, (2026). Cancer risk statistics | Cancer Research UK [online]. Cancer Research UK | The world's leading cancer charity. [Viewed 01 June 2026]. Available from: https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/all-cancers-combined/risk 

McNeil, A. and Arena, R., (2017). The evolution of health literacy and communication: introducing health harmonics. Progress in Cardiovascular Diseases [online]. 59(5), 463–470. [Viewed 01 June 2026]. Available from: doi: 10.1016/j.pcad.2017.02.003

Traynor, G., Shearn, A. I., Milano, E. G., Ordonez, M. V., Velasco Forte, M. N., Caputo, M., Schievano, S., Mustard, H., Wray, J. and Biglino, G., (2022). The use of 3D-printed models in patient communication: a scoping review. Journal of 3D Printing in Medicine [online]. 6(1), 13–23. [Viewed 01 June 2026]. Available from: doi: 10.2217/3dp-2021-0021