Quantifying the impact of population shift across age and sex for abdominal organ segmentation
File(s)
Accepted version
OA Location
Author(s)
Čevora, Kate
Glocker, Ben
Bai, Wenjia
Type
Conference Paper
Abstract
Deep learning-based medical image segmentation has seen tremendous progress over the last decade, but there is still relatively little transfer into clinical practice. One of the main barriers is the challenge of domain generalisation, which requires segmentation models to maintain high performance across a wide distribution of image data. This challenge is amplified by the many factors that contribute to the diverse appearance of medical images, such as acquisition conditions and patient characteristics. The impact of shifting patient characteristics such as age and sex on segmentation performance remains relatively under-studied, especially for abdominal organs, despite that this is crucial for ensuring the fairness of the segmentation model. We perform the first study to determine the impact of population shift with respect to age and sex on abdominal CT image segmentation, by leveraging two large public datasets, and introduce a novel metric to quantify the impact. We find that population shift is a challenge similar in magnitude to cross-dataset shift for abdominal organ segmentation, and that the effect is asymmetric and dataset-dependent. We conclude that dataset diversity in terms of known patient characteristics is not necessarily equivalent to dataset diversity in terms of image features. This implies that simple population matching to ensure good generalisation and fairness may be insufficient, and we recommend that fairness research should be directed towards better understanding and quantifying medical image dataset diversity in terms of performance-relevant characteristics such as organ morphology.
Date Issued
2025-01-01
Date Acceptance
2024-10-01
Citation
Lecture Notes in Computer Science, 2025, 15198, pp.88-97
ISBN
9783031727863
ISSN
0302-9743
Publisher
Springer Nature Switzerland
Start Page
88
End Page
97
Journal / Book Title
Lecture Notes in Computer Science
Volume
15198
Copyright Statement
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
MICCAI Workshop on Fairness of AI in Medical Imaging
Subjects
Artificial Intelligence & Image Processing
Publication Status
Published
Start Date
2024-10-06
Finish Date
2024-10-10
Coverage Spatial
Marrakesh, Morocco
Date Publish Online
2024-10-13