Medical image segmentation—the process of delineating anatomical structures or regions of interest within medical images—is notoriously time-consuming and labor-intensive. Historically, this process requires extensive manual input from trained radiologists, which can lead to expensive outsourcing with a third-party vendor or an in-house backlog of cases for applications like pre-surgical planning, medical device sizing, custom-made device planning, and post-market surveillance.
In recent years, AI-driven machine learning algorithms have automated and accelerated segmentation tasks with unprecedented speed and accuracy—which offers significant advantages when it comes to building a personalized program at scale. To overcome some of the challenges inherent to DICOM medical images, these AI-powered tools recognize complex patterns and features within images, allowing them to segment anatomical structures with minimal hands-on intervention.
Automated segmentation not only reduces the workload for in-house planners and vendor teams, but it also improves image analysis overall. And as AI continues to evolve, it holds the potential to further streamline medical imaging workflows and truly operationalize personalized medicine at scale for the first time.
Selecting the right segmentation and 3D solution is crucial for leveraging these advancements effectively. Here are a few considerations for choosing the right solution.
1. Accuracy and Precision
Pain point: Even the smallest errors in the process of mapping and measuring anatomical structures can have huge consequences on device fit, product performance, and ultimately, case outcomes.
What to consider: Ensure that the segmentation and 3D solution you select uses an algorithm that has been validated and tested against clinical benchmarks. This will give you peace of mind about the reliability of the analysis.
2. Integration and Usability
Pain point: Within a validated quality system, a new solution can be deployed in a way that is disconnected from other processes and creates more manual work for engineers, product managers, case planners, and sales reps.
What to consider: In order to operationalize a new tool, it should seamlessly integrate with your current workflows to ensure interoperability and smooth data sharing across platforms. Equally important is the ease of use. Some segmentation software can only run on bulky laptops with a large hard drive, which is not convenient for sales reps or clinical specialists planning cases on the go. Opt for solutions with an intuitive user interface and easy cloud-based access that isn’t tethered to one location or device.
3. Customization and Flexibility
Pain point: No two quality systems are exactly alike—and a one-size-fits-all solution fits no one.
What to consider: Every device manufacturer knows that the systems and processes you work within are unique to your R&D, manufacturing, and quality control processes. A customizable solution can actually allow you to close gaps in your existing process without dismantling validated workflows. A customizable solution gives you the flexibility to adapt the technology to your specific requirements—not the other way around.
4. Cost and Value
Pain point: It can be difficult to get buy-in on the upfront investment needed for a new solution.
What to consider: While cost is a significant factor when it comes to decision making, it’s important to consider the overall value the solution provides rather than just the upfront price. Assess the cost in relation to the benefits offered, such as improved patient outcomes, efficiency gains, and long-term savings.
Additionally, factor in the costs associated with scaling the solution, including the training and salaries of employees required to effectively implement and maintain the system, as well as any additional resources needed for integration. A comprehensive cost-benefit analysis should encompass these elements to provide a clear picture of the total investment. This approach will help you make an informed decision that aligns with your budget and goals, ensuring that you achieve the best possible return on investment.
5. Scalability and Future-Proofing
Pain point: By the time a new technology is working well within the quality system, it’s outdated.
What to consider: Technology evolves rapidly, and it’s important to choose a solution that can scale with your growing needs and adapt to future advancements. Look for solutions that offer modularity, automation, and the capability to incorporate new features and updates as technology progresses.
AI and machine learning play a crucial role in this scalability, enabling systems to learn and improve over time, handle increasing amounts of data, and offer more sophisticated analyses. Evaluate how your resources are affected by growth. This future-proofing ensures that your investment remains relevant and valuable over time.
The Bottom Line
Selecting the right segmentation and 3D solution is a critical decision for medical device and medtech companies. This choice directly impacts whether or not you can deliver personalized medicine at scale.
Are you ready to explore a different way to segment your data?
At Axial3D, our platform offers robust scalability for your custom and patient-matched devices, utilizing patented technology powered by advanced machine learning algorithms and AI-enabled automation.
