Medical 3D visualisation
Designing a better user experience for image-guided surgical planning
L.E. Ruijs (TU Delft - Industrial Design Engineering)
R.H.M. Goossens – Graduation committee member (TU Delft - Industrial Design Engineering)
S.C. Pont – Mentor (TU Delft - Industrial Design Engineering)
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Abstract
Pancreatic cancer is one of the deadliest types of cancer and generally has a 5-year survival rate of 5%. Surgery (pancreatoduodenectomy) is the only form of curative treatment for patients diagnosed with pancreatic cancer. If the tumour is operable, the resectability of the tumour depends on the level of tumour-vessel contact. Based on CT scans, assessing the resectability of the tumour can be critical and demands expertise.
Philips, collaborating with Catharina Hospital Eindhoven and Eindhoven University of Technology (TU/e), has an oncology team called Eindhoven MedTech Innovation Centre (e/MTIC), working on a health care innovation regarding Pancreatic cancer enabled by AI (artificial intelligence) and Medical Image Guiding. This graduation project focuses on this pancreas use case; an integrated imaging workstation is being developed using 3D visualisation and enabling AI to enhance the workflow.
This graduation project focuses on improving healthcare professionals' user experience and interaction within an integrated imaging workstation. The project focuses on visually communicating tumour detection and vascular segmentation, resulting in better diagnosis and surgical planning. The aim is to eventually design a visual language (a design language system guideline) to improve the workflow and experience.
A user review was conducted using videos of user tests performed previously in the pancreas use case. This review led to the usability problems and the user needs of the 3D model in the pancreatic use case. A professional critical study was also conducted, revealing the prototype's strengths and weaknesses.
Eventually, a redesign for the 3D model was developed. This model is focused on showing resectability to improve the workflow. Moreover, uncertainty regarding the tumour size and shape is visualised as well.
Additionally, a DLS guideline for similar applications within Philips focused on medical 3D visualisation was designed at the end of this thesis.
Finally, 3D visualisation, enabled by AI, improves the workflow within a medical imaging workstation.