About the Collection
3D printing is helping surgeons improve patient outcomes, and assisting them in pre-operative planning, intraoperative visualization, surgical training and patient communications. As the future of surgery continues to shift to be more patient-specific, the need for cloud-based medical image segmentation for 3D printing that can be deployed across entire departments and hospitals around the world will become increasingly important. These new cloud-based technologies and novel machine learning techniques will allow hospitals to easily start and scale their efforts in medical 3D printing, with equal or better accuracy than manual segmentation.
The development of cloud-based technologies, which do not require licenses and significant IT infrastructure, allow hospitals to start benefiting, with minimal barriers to entry. Hospitals and organizations with established in-house 3D print labs can also utilize novel machine learning techniques, which provide both accurate and scalable segmentation workflows to allow teams to not only speed up their ability to provide models but increase their capacity significantly to allow them to meet the hospitals clinical needs.
3D printing continues to provide both tangible and intangible value by reducing time in the OR, improving patient communication and facilitating multidisciplinary discussion among teams with the ultimate goal to improve patient outcomes.
The collection of articles was sponsored by Axial3D, and the submitted manuscripts have undergone the journal’s standard peer-review process.