LAUSR

AbstractPurposeThere is a paucity of methods to model soft anatomical tissues. Accurate modelling of these tissues can be difficult with current medical imaging technology. MethodsThe aim of this research was to develop a methodology to model non-intestinal colorectal tissues that are not readily identifiable radiologically to enhance contextual understanding of these tissues and inform medical device design. The models created were used to inform the design of a novel medical device to separate the mesocolon from the retroperitoneum during resection of the colon. We modelled the peritoneum and the mesentery. The mesentery was used to indicate the location of Toldt’s fascia.ResultsWe generated a point cloud dataset using cryosection images as the target anatomy is more visible than in CT or MRI images. The thickness of the mesentery could not be accurately determined as point cloud data do not have thickness. A denser point cloud detailing the mesenteric boundaries could be used to address this.ConclusionsExpert anatomical and surgical insight and point cloud data modelling methods can be used to model soft tissues. This research enhances the overall understanding of the mesentery and Toldt’s fascia in the human specimen which is necessary for medical device innovations for colorectal surgical procedures.