The medical imaging community generates a wealth of datasets, many of which are openly accessible and annotated for specific diseases and tasks such as multi-organ or lesion segmentation. Current practices continue to limit model training and supervised pre-training to one or a few similar datasets, neglecting the synergistic potential of other available annotated data. We propose MultiTalent, a method that leverages multiple CT datasets with diverse and conflicting class definitions to train a single model for a comprehensive structure segmentation. Our results demonstrate improved segmentation performance compared to previous related approaches, systematically, also compared to single dataset training using state-of-the-art methods, especially for lesion segmentation and other challenging structures. We show that MultiTalent also represents a powerful foundation model that offers a superior pre-training for various segmentation tasks compared to commonly used supervised or unsupervised pre-training baselines. Our findings offer a new direction for the medical imaging community to effectively utilize the wealth of available data for improved segmentation performance. The code and model weights will be published here: [tba]

翻译：医学影像领域产生了大量数据集，其中许多公开可用且针对特定疾病和任务（如多器官或病灶分割）进行了标注。当前实践仍将模型训练和监督预训练局限在单个或少数相似数据集上，忽略了其他可用标注数据的协同潜力。我们提出多才华（MultiTalent），一种利用多个具有不同且冲突类别定义的CT数据集来训练单一模型以进行综合结构分割的方法。实验结果表明，与先前相关方法相比，我们的方法在分割性能上具有系统性提升；与使用最新方法进行的单数据集训练相比，尤其对于病灶分割和其他具有挑战性的结构，性能更优。我们还证明，多才华同时是一种强大的基础模型，为各种分割任务提供了优于常用监督或无监督预训练基线的预训练效果。我们的发现为医学影像领域有效利用丰富可用数据以提升分割性能指明了新方向。代码和模型权重将发布在此处：[待定]