Toward requirement-based performance in data-driven image analysis algorithms for robotic applications

Accurate and reliable medical image analysis, particularly in lung nodule segmentation, plays a crucial role in data-driven healthcare assistance technologies. Current evaluation metrics for segmentation algorithm performance, however, lack specificity to individual use cases and may not adequately assess the accuracy of 2D segmentation in context. In this preliminary work, we propose a novel evaluation approach that incorporates use case-specific evaluation metrics, focusing particularly on the spatial congruence and mass center accuracy of the nodule segmentation in the context of robot-assisted image-guided interventions. By simulating predicted segmentation masks using distortion techniques applied to ground truth masks from the LIDC-IDRI dataset, we compute the Dice score and Hausdorff distance, two common metrics for segmentation algorithm evaluation, as well as two proposed metrics, a Mass Center and a Centered Overlap score. Our preliminary findings indicate that the proposed metrics are superior to traditional ones, providing a more descriptive evaluation within the context of the intended use case. Future work will include comprehensive assessments of more extensive simulation techniques as well as the development and evaluation of a custom segmentation algorithm trained using our proposed metrics. By promoting the adoption of use case-specific metrics, we aim to improve the performance of segmentation algorithms, and ultimately, the outcome of critical healthcare procedures.