Can I read IODeep: an IOD for the introduction of deep learning in the DICOM standard on EtoBox?

IODeep: an IOD for the introduction of deep learning in the DICOM standard by Contino, Salvatore; Cruciata, Luca; Gambino, Orazio; Pirrone, Roberto is a scholarly article available to read on EtoBox.

What is IODeep: an IOD for the introduction of deep learning in the DICOM standard about?

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 pu

Author

Contino, Salvatore; Cruciata, Luca; Gambino, Orazio; Pirrone, Roberto

Published

2023

Language

EN