用人体模型扫描数据构建公开基准集，提升AI在不同设备下的通用性。
A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization
- 基于人体模型的多中心CT扫描数据，控制个体差异影响。
- 包含1378个图像序列，覆盖4家厂商13台设备、8个机构及多种剂量。
- 提供评估方法与代码，助力开发图像与特征级稳定性的AI调和技术。
人工智能在医疗领域带来巨大机遇，但在数据分布变化时泛化能力差。针对基于CT的AI分析，扫描设备、重建方式或剂量变化会引发显著数据分布偏移。本文提出一个开源基准数据集，包含1378个图像序列，由4家厂商的13台扫描仪在8个机构间，按统一协议采集的人体模型CT影像，涵盖多种剂量设置。使用人体模型可消除患者间与患者内差异的影响。研究还提供了评估图像级与特征级稳定性及肝脏组织分类的流程、基线结果与开源代码，推动AI调和技术的发展。
原文摘要 · Abstract (English)
Artificial intelligence (AI) has introduced numerous opportunities for human assistance and task automation in medicine. However, it suffers from poor generalization in the presence of shifts in the data distribution. In the context of AI-based computed tomography (CT) analysis, significant data distribution shifts can be caused by changes in scanner manufacturer, reconstruction technique or dose. AI harmonization techniques can address this problem by reducing distribution shifts caused by various acquisition settings. This paper presents an open-source benchmark dataset containing CT scans of an anthropomorphic phantom acquired with various scanners and settings, which purpose is to foster the development of AI harmonization techniques. Using a phantom allows fixing variations attributed to inter- and intra-patient variations. The dataset includes 1378 image series acquired with 13 scanners from 4 manufacturers across 8 institutions using a harmonized protocol as well as several acquisition doses. Additionally, we present a methodology, baseline results and open-source code to assess image- and feature-level stability and liver tissue classification, promoting the development of AI harmonization strategies.
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