Magnetic Resonance Imaging (MRI), a foundational technique in medical imaging, presents a non-invasive means of acquiring detailed visualizations of anatomical structures. This intricate process generates images that hold pivotal value across medical domains, from diagnosing diseases to devising treatment strategies and advancing scientific research. A cornerstone of MRI's efficacy lies in accurate "segmentation" – the process of partitioning the images into distinct regions of interest, enabling targeted analysis and interpretation.

However, the reliability of MRI segmentation data often contends with the potential for errors or inconsistencies, stemming from various sources such as imaging conditions and human interpretation. Recognizing this, the significance of "quality control mechanisms" becomes pronounced.

The focal point of this research project has been the development of an automated quality control (QC) system tailored specifically for segmentation of brain structures from human MRI images. The fundamental objectives that underscored this venture encompassed two distinct yet interconnected aims: the augmentation of user interaction and data analysis capabilities within the Flywheel platform by leveraging the capabilities of the Postman API, and the construction of an artificial intelligence (AI) mechanism that could efficiently automate the process of quality control. We also utilized tools for identifying and isolating topological errors within brain MRI segmentation. These errors, particularly in the form of holes and loops within the segmentation, can adversely impact the reliability and precision of subsequent analyses. We processed a dataset of over 10,000 segmentation images. These data points were then used to further enhance MRI quality control artificial intelligence mechanisms as well as describe a relation between topological errors and quality control issues.

In conclusion, the construction of a new QC workflow, utilizing these newly developed tools, enables seamless integration of user feedback and the quantitative analysis of topological errors. This integration contributes to improved data quality, ensuring the reliability of MRI segmentation data for future medical applications.