3D Medical Image Processing Software
Description
A medical company with multiple international branches was facing a major challenge. First of all, they could not find qualified software engineering experts that could tackle their specific requests. That is, the manual search for organ placement boundaries using images is a time-consuming process and they were looking for a way to automate the process. A custom state-of-the-art 3D imaging software was to be developed to address this specific challenge of the client. After presenting the vision and reviewing our technological expertise, the development was entrusted to us, and our team began working to find a solution to this unique challenge.
Challenge
To tackle the client's challenge of automating the process of determining specific organ placement boundaries from images, we first cooperated with a specialized research institute for guidance. The research, which took three weeks and cost $5,000, helped us understand which direction to take. Next up, we had to perform a careful analysis of research articles on similar image processing problems to help us innovate and come up with a new solution.
Solution
Here's how we achieved a successful outcome for our client from prototype to a final solution.
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Dataset Analysis: First, we requested existing datasets the medical company could provide or any available datasets that could help us.
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Analysis of Current Approaches: In the next step, we analyzed the latest approaches in the image recognition field, researched, and reviewed scientific articles for image recognition in the medical industry, and cooperated with a specialized research institute to find a solution.
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Testing Promising Approaches: We tested promising approaches for achieving result accuracy and performance, implementing a working prototype that met the required parameters (speed and number of accurately processed cases) in just three weeks.
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Pre and Post-Processing: We preprocessed and post-processed data using methods of discrete mathematics and graph operations.
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First Product Version: Once the main risks were removed, we developed the first version of the full-fledged product in just 3 months. The first version of the product was built using neural networks. The next step was to increase accuracy and increase team involvement.
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Final Solution: We successfully developed a 3D Imaging Software that successfully converts raw 3D models into semantically meaningful data and operates on three-dimensional parts of the models.
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Current State: We have successfully developed and continuously enhanced a highly complex 3d software system for interacting with organs over the past 4 years for our client, fully customized to meet their needs and challenges.
To support the company's plan to integrate the software with other information systems, we designed it as a series of microservices. These microservices are tailored to address various medical image recognition challenges, ensuring seamless integration with any external system. We continue to provide ongoing development and support services to the medical company.
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