IODeep: An IOD for the introduction of deep learning in the DICOM standard - Contino, Salvatore; Cruciata, Luca; Gambino, Orazio; Pirrone, Roberto (Computer methods and programs in biomedicine, 2024)
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. 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. 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. 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. •The information architecture of IODeep, and the DICOM compliant workflow for both ROI prediction and storage on the PACS.•A novel algorithm using the IODeep tags to match the DNN for the visualized study in a transparent way for the doctor.•A purposely designed back-end independent JSON format to describe DNNs to be used as a network description inside IODeep.•A purposely designed PACS viewer running all the client operations for using IODeep, with no modifications on the server.•A REST service implementation of the whole IODeep workflow, running outside the PACS infrastructure.