Introduction

In the field of medical image processing, innovation plays a crucial role in revolutionizing diagnostic methodologies and improving patient outcomes. This case study looks at a Hungarian company that started with 20 people and grew to a team of 80. They worked on two medical image processing projects: one focused on improving breast cancer diagnostics, and the other on building support software to help doctors measure joint gaps and erosion in human extremities.

The Story

The company worked on two main medical image processing projects: one to improve breast cancer diagnostics, and another to support physicians in measuring joint gaps and erosion. For the breast cancer project, the team analyzed low-resolution Dicom images of breast tissue that had been transilluminated using high-brightness LED diodes. These images were processed with neural networks to help detect changes in the tissue without needing invasive procedures like biopsies. At the same time, they developed software to help doctors assess joint gaps and erosion in hands and feet, aiming to improve diagnostic accuracy and support treatment planning

The Challenge

The primary challenge faced by the company was finding suitable software to display medical images and facilitate interactions for both projects. This involved devising test plans, strategies, and scenarios for implementation, as well as manual testing and refining of algorithms for neural network analysis. The complexity of the task required careful consideration of various software options to ensure compatibility with the team's requirements and objectives

The Conclusion

After meticulous evaluation and testing of several software solutions, the company successfully identified a suitable platform that met their needs for both projects. This software enabled the team to display medical images effectively, perform necessary interactions, and refine neural network algorithms for accurate analysis. With the chosen software in place, the company made significant strides in advancing breast cancer diagnostics and physician support capabilities, contributing to improved patient care and outcomes. The successful outcome underscored the team's dedication, expertise, and collaborative efforts in the field of medical image processing.