Spann, ConorAl-Maliki, ShathaBoué, FrançoisLutton, ÉvelyneVidal, FranckPeter VangorpMartin J. Turner2022-08-162022-08-162022978-3-03868-188-5https://doi.org/10.2312/cgvc.20221171https://diglib.eg.org:443/handle/10.2312/cgvc20221171Most medical imaging studies into human digestion focus on the organs themselves and neglect the content under digestion. Instead, analysing food inside digestive organs and any subsequent motion can provide valuable information about the digestive tract. This study is part of a larger project, with previous work done to automatically detect peas in a human stomach from MRI scans but it produced too many false positives. Our study therefore aims to accurately visualise peas in a human stomach whilst also providing facilities to correct the mistakes made by the previous pea detection. Our solution is a visualisation and correction tool split into 2D and 3D visualisation areas. The 2D areas show three sequential stomach slices with detected peas as green circles and allows the user to correct the pea detection. Peas can be added, removed or marked as unsure. The 3D area shows a Marching Cubes rendering of the stomach with spherical glyphs as the peas. Due to the way the data was acquired, some pea motion was also visualised. Aside from difficulties interpreting the data due to acquisition artefacts, our tool was found to be very easy to use, with some minor improvement suggestions for interacting with the images. Overall, the software achieved its aims of visualising the peas and stomach whilst also providing methods to correct the pea data. Future work will look into improving the pea detection and more work into following the pea motion.CCS Concepts: Computing methodologies → Machine learning; Human-centered computing → Empirical studies in visualization; Applied computing → Systems biologyComputing methodologies → Machine learningHumancentered computing → Empirical studies in visualizationApplied computing → Systems biology10.2312/cgvc.2022117147-548 pages