Axial3D Partners with GE HealthCare for DICOM-to-Medical Model 3D Printing

December 4, 2024 by Joris Peels 3D Printing 3D Printing Materials 3D Software Medical 3D Printing

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The path of medical 3D printing firm Axial3D’s is unsurprising. The firm’s increasing emphasis on AI aligns with its previous moves: partnering with GE, opening a new facility to accommodate growth, securing a $10 million investment from Stratasys, collaborating with Dutch researchers for EU funding, working with Swiss hospitals, and entering the medical training sector. These actions collectively underscore a clear, logical progression. By methodically building partnerships and advancing step by step, the company is steadily positioning itself to dominate the patient-specific software market. Its understated approach—eschewing arrogance or overhyping its vision—renders it an inevitable presence in the industry.

Now, the company is partnering once again with GE HealthCare to deliver patient-specific MRI models. Organizations with MRI clients can integrate Axial3D’s AI-based segmentation software with GE’s imaging software. Axial3D’s Insight platform promises to reduce the time required for technicians to segment existing DICOM imagery. The company also suggests that, in conjunction with GE’s software, fewer scans may be needed to create patient-specific models. As indicated in previous releases, the platform incorporates patient-specific model creation and automates the processes of checking, ordering, and shipment.

This means that Axial3D can not only make life easier for GE´s customers but also let them make patient specific models faster while potentially opening up another revenue stream for GE. At the same time the files could be used for patient specific planning and may be used for things such as surgical guides and perhaps custom implants in the future. Through making an easy to adopt solution that can let you with a few clicks order a 3D printed part Axial3D is making seamless and painless adoption of patient specific models possible for hospitals. It is also enmeshing itself in the connective tissue between hospital and outside provider as well as devices. This is such a beautiful place to be. In effect Axial3D is building a middleware layer for hospitals that can be monetized in various ways. It is seamlessly making itself inevitable and unmissable.

¨We are excited to expand our collaboration with GE HealthCare and bring our innovative 3D modeling solutions to a broader audience. This collaboration reinforces our mission to make patient-specific care routine and scalable. With GE HealthCare’s oZTEo scanning technology and our AI-driven segmentation, medical professionals can seamlessly receive 3D models from their MR images that significantly enhance patient education and clinical communication,” said Axial3D CEO Roger Johnston.

¨Our collaboration with Axial3D aligns with our goal of advancing precision healthcare. By integrating Axial3D’s powerful 3D modeling capabilities with our MR scanning technology, we are offering our customers a new level of capability to enhance the patient experience through better understanding of their unique situation, ultimately helping to improve patient outcomes,” added Maggie Fung, Director of Musculoskeletal MR, GE HealthCare.

DICOM conversion for 3D printing has always been challenging, and MRI file segmentation equally so. Many firms have attempted to address these complexities over the years. Axial3D is now advancing by mastering the machine-to-model segment of this process. By simplifying adoption, the company is driving growth, and its partnerships with hospitals, researchers, and OEMs ensure continued expansion. Solutions that streamline the creation, management, and ordering of 3D prints are undeniably necessary. However, many so-called “solutions” fall short, offering little practical value to overburdened technicians managing a constant patient flow. For hospitals, many tools demand excessive initial investment or effort, making them impractical. Axial3D demonstrates that simplicity can drive growth and make its platform indispensable. While this approach is proven effective for medical models, it holds promise for other applications as well.