Background and Objective: In recent years, Artificial Intelligence (AI) and in particular Deep Neural Networks (DNN) became a relevant research topic in biomedical image segmentation due to the availability of more and more data sets along with the establishment of well known competitions. Despite the popularity of DNN based segmentation on the research side, these techniques are almost unused in the daily clinical practice even if they could support effectively the physician during the diagnostic process. Apart from the issues related to the explainability of the predictions of a neural model, such systems are not integrated in the diagnostic workflow, and a standardization of their use is needed to achieve this goal. Methods: This paper presents IODeep a new DICOM Information Object Definition (IOD) aimed at storing both the weights and the architecture of a DNN already trained on a particular image dataset that is labeled as regards the acquisition modality, the anatomical region, and the disease under investigation. Results: The IOD architecture is presented along with a DNN selection algorithm from the PACS server based on the labels outlined above, and a simple PACS viewer purposely designed for demonstrating the effectiveness of the DICOM integration, while no modifications are required on the PACS server side. Also a service based architecture in support of the entire workflow has been implemented. Conclusion: IODeep ensures full integration of a trained AI model in a DICOM infrastructure, and it is also enables a scenario where a trained model can be either fine-tuned with hospital data or trained in a federated learning scheme shared by different hospitals. In this way AI models can be tailored to the real data produced by a Radiology ward thus improving the physician decision making process. Source code is freely available at https://github.com/CHILab1/IODeep.git

翻译：背景与目标：近年来，随着数据集日益丰富以及知名竞赛的持续举办，人工智能（AI）尤其是深度神经网络（DNN）在生物医学图像分割领域成为重要研究课题。尽管基于DNN的分割方法在研究层面广受欢迎，但这些技术在日常临床实践中几乎未被采用，即使它们能够在诊断过程中为医生提供有效支持。除神经网络模型预测的可解释性问题外，此类系统尚未融入诊断工作流程，亟需标准化规范以实现这一目标。方法：本文提出IODeep——一种新型DICOM信息对象定义（IOD），旨在存储已在特定图像数据集上完成训练的DNN权重与架构，该数据集根据采集模态、解剖区域及研究疾病进行标注。结果：本文阐述了该IOD架构，以及基于上述标注从PACS服务器选取DNN的算法，并设计了一个简易PACS查看器用于验证DICOM集成的有效性，同时无需对PACS服务器端进行任何修改。此外，还实现了一套支持完整工作流程的基于服务的架构。结论：IODeep确保训练完成的AI模型能够完全集成至DICOM基础设施中，并支持两种应用场景：既可使用医院数据对预训练模型进行微调，也可通过不同医院共享的联邦学习方案进行训练。通过这种方式，AI模型可针对放射科产生的真实数据进行定制化调整，从而提升医生的决策过程。源代码可自由获取于：https://github.com/CHILab1/IODeep.git