Unlocking Robust Segmentation Across All Age Groups via Continual Learning
This addresses the challenge of robust medical imaging segmentation for all age groups, particularly improving pediatric care, but is incremental as it adapts existing methods.
The paper tackled the problem of deep learning models trained on adult data underperforming on pediatric CT images for organ segmentation, and achieved high segmentation accuracy across age groups with a continual learning model, scoring Dice of 0.90 on adult and 0.84 on pediatric data.
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).