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).

本文针对医学影像学中的深度学习模型多基于成人数据而在儿科影像上的性能不明确的问题进行研究。我们评估了基于成人数据训练的 CT 器官分割算法在儿科 CT 体积上的性能，并发现明显与年龄相关的性能下降。随后，我们提出并评估了一些策略，包括数据增强和持续学习方法，以实现在所有年龄群体中获得良好的分割准确性。我们表现最佳的模型采用了持续学习方法进行训练，在成人和儿科数据上都取得了高分割准确性（Dice 分数分别为 0.90 和 0.84）。

通过持续学习实现不同年龄组的稳健分割

Unlocking Robust Segmentation Across All Age Groups via Continual  Learning

In this study, we present a method for generating automated anatomy
segmentation datasets using a sequential process that involves nnU-Net-based
pseudo-labeling and anatomy-guided pseudo-label refinement. By combining
various fragmented knowledge bases, we generate a dataset of whole-body CT
scans with $142$ voxel-level labels for 533 volumes providing comprehensive
anatomical coverage which experts have approved. Our proposed procedure does
not rely on manual annotation during the label aggregation stage. We examine
its plausibility and usefulness using three complementary checks: Human expert
evaluation which approved the dataset, a Deep Learning usefulness benchmark on
the BTCV dataset in which we achieve 85% dice score without using its training
dataset, and medical validity checks. This evaluation procedure combines
scalable automated checks with labor-intensive high-quality expert checks.
Besides the dataset, we release our trained unified anatomical segmentation
model capable of predicting $142$ anatomical structures on CT data.

通过使用基于 nnU-Net 的伪标签和解剖导向的伪标签细化的顺序过程，结合各种碎片化的知识库，我们生成了一个包含 142 个体素级标签对 533 个体积进行了全面解剖覆盖的全身 CT 扫描数据集，该数据集已获得专家批准。我们的方法在标签整合阶段不依赖于手动注释。使用人类专家评估、在 BTCV 数据集上进行的深度学习有用性基准测试（在不使用其训练数据集的情况下实现了 85% 的 Dice 分数）以及医学有效性检查，我们检验了其合理性和实用性。除数据集外，我们还发布了能够对 CT 数据上的 142 种解剖结构进行预测的训练好的统一解剖分割模型。