Reproducibility: The Key to Robust AI and Medical Image Analysis

Abstract

Scientific progress hinges on the open exchange of knowledge and data. However, replicating research findings in medical image analysis presents a distinct challenge. Unlike other scientific fields, achieving true reproducibility in this domain requires a vast amount of intricate data. This encompasses the raw image data itself, the specific acquisition protocols employed, the software utilized for analysis, and the precise processing settings applied.

This presentation delves into the challenges and solutions associated with ensuring reproducibility in medical image analysis. We will showcase 3D Slicer as a practical example to demonstrate its capabilities in analyzing individual patient data. This includes handling complex, multi-modal datasets relevant to surgical planning. 3D Slicer offers advanced visualization tools essential for surgeons and physicians during minimally invasive procedures. Additionally, 3D Slicer facilitates quality assurance of segmentation results, ensuring the accuracy and consistency of extracting crucial information from medical images.

The subsequent section of the presentation shifts the focus to analyzing data from entire patient cohorts. We will emphasize the critical role of accessible and standardized databases that adhere to the FAIR data principles (Findable, Accessible, Interoperable, Reusable). The NCI's Imaging Data Commons serves as a model for such a repository.

The solutions and recommendations presented aim to enable a future where medical image analysis research is more reliable, and findings can be easily replicated by others, leading to faster advancements in the field.