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Abstract #2129

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

Qing Li1, Yan Li2, Longyu Sun1, Mengting Sun1, Meng Liu1, Xumei Hu1, Xinyu Zhang1, Xueqin Xia3, Shuo Wang4, Yinghua Chu5, and Chengyan Wang1
1Human Phenome Institute, Fudan University, Shanghai, China, 2Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 3Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 4Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China, 5Simens Healthineers Ltd, Shanghai, China

Synopsis

Keywords: Visualization, Visualization, Fairness; Bias

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.

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Keywords