Unlocking Robust Segmentation Across All Age Groups via Continual Learning
arxiv(2024)
摘要
Most deep learning models in medical imaging are trained on adult data with
unclear performance on pediatric images. In this work, we aim to address this
challenge in the context of automated anatomy segmentation in whole-body
Computed Tomography (CT). We evaluate the performance of CT organ segmentation
algorithms trained on adult data when applied to pediatric CT volumes and
identify substantial age-dependent underperformance. We subsequently propose
and evaluate strategies, including data augmentation and continual learning
approaches, to achieve good segmentation accuracy across all age groups. Our
best-performing model, trained using continual learning, achieves high
segmentation accuracy on both adult and pediatric data (Dice scores of 0.90 and
0.84 respectively).
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