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：摘要：背景与目标：近年来，随着数据集日益丰富及相关知名竞赛的建立，人工智能特别是深度神经网络在生物医学图像分割领域成为重要研究课题。尽管基于深度神经网络的分割方法在研究领域广受欢迎，但这些技术在日常临床实践中几乎未被采用，即便它们能够有效辅助医生进行诊断。除神经模型预测的可解释性问题外，此类系统尚未整合至诊断工作流程中，且需建立标准化规范以实现该目标。方法：本文提出IODeep这一新型DICOM信息对象定义，旨在存储已在特定图像数据集上预训练的深度神经网络的权重与架构，该数据集需标注采集模态、解剖区域及所研究疾病。结果：本文展示了该IOD架构、基于上述标注标签从PACS服务器中选择深度神经网络的算法，以及为验证DICOM集成有效性而专门设计的简易PACS浏览器（无需修改PACS服务器端）。同时实现了支持完整工作流的服务型架构。结论：IODeep可确保训练完成的AI模型与DICOM基础设施完全集成，并支持两种应用场景：利用医院数据对预训练模型进行微调，或通过不同医院共享的联邦学习方案训练模型。由此，AI模型可根据放射科室产生的真实数据进行定制，从而改善医生的决策过程。源代码已在https://github.com/CHILab1/IODeep.git 开源。