Synopsys has launched a significant update to Simpleware ScanIP software, which widens its capabilities for segmenting anatomical regions through a new module Simpleware AS Ortho (Auto Segmenter for Orthopedics).
Based on machine learning (ML)-based auto segmentation module, the new product builds on Synopsys’ ScanIP software, a solution for 3D image processing and segmenting images produced by computed tomography (CT) or magnetic resonance imaging (MRI) scanners.
The newly launched module is for segmentation needs in the hips and knees.
It provides the users with a 20 to 50 times faster rate of segmentation for clinical images when applied with Synopsys’ ScanIP software to run their analysis.
Moreover, this fully scalable technology offers more consistency and increased reliability in biomechanical compatibility.
It can also help to streamline the workflow process in high-fidelity patient-specific models, surgical tools and bespoke implants.
Synopsys engineering vice president Terry Ma said: “The demand for image-based modelling of human anatomy tools with ML-enabled intelligence is rapidly growing, especially in markets that include patient-specific workflows for medical devices, surgical guides and planning, and in silico clinical trials.
“We’re looking forward to collaborating with more medical device companies to solve their long-standing image segmentation challenges.”
According to research, total knee arthroplasties (TKAs) in the USA will grow from 719,000 in 2015 to 3.48 million by 2030.
Total hip arthroplasties (THAs) is estimated to almost double from 332,000 to 572,000 in the same period.
The company is initially launching Simpleware AS Ortho Auto as a segmenter for orthopaedics.
Further modules will be added in time to enable automated segmentation options for other anatomical regions, said Synopsys in a press release.