Evaluation of the fairness and effectiveness of nnU-Net on multi-organ segmentation

Motivation: A systematic analysis of the segmentation effectiveness for fairness helps enhance the effectiveness of artificial intelligence(AI) model, which has not been done before. Goal(s): This study aims to compile statistics the relation between the segmentation effectiveness and aging, gender as well as anatomical regions. Approach: The nnU-Net model is used for organ segmentation while the DICE was computed to evaluate the relation between the effectiveness with aging and gender and the heatmap was used to visualize the spatial error distribution regarding anatomical regions. Results: The result demonstrates variations in nnU-Net's effectiveness within subgroups, highlighting the significance of attention mechanisms for segmentation model enhancement. Impact: This study comprehensively evaluated the fairness and effectiveness of nnU-Net across multiple organs within the body. An analysis was conducted to investigate the relationship between segmentation errors and age, gender as well as anatomical regions for organ segmentation.