When working with clinical CT and MRI data towards building a patient-specific surgical plan, one of the most significant bottlenecks involves manual segmentation and landmarking. In this context, segmentation is the identification of different regions of interest (ROIs) within the anatomy, such as bone, cartilage and the implant. At this stage, a great deal of time might be spent on carrying out the first stages of segmentation through manual and laborious processes.
A new solution to this problem, then, is provided by Simpleware AS Ortho, which frees up time by using AI-powered Machine Learning (ML) algorithms to automate initial segmentation. Rather than spend time identifying different anatomical regions manually, these are automatically inferred by the software, with a standard segmentation result including masks and landmarks as a 2D and 3D output. The automated segmentation distinguishes between femurs, pelvis and sacrum, and generates common landmarks, with additional options to edit the results, and export the final model for CAD or FEA. In the area of orthopedic revision planning, considerable savings are made in terms of time spent on each case, resulting in more efficient workflows.