Developing a smart pacs: Cbir system using deep learning

With the growing number of digital medical imaging records, the need for an automatic procedure to retrieve only data of interest is of increasing importance. A Picture Archiving and Communication System (PACS) provides effective storage and retrieval based on TAGs but does not allow us for query by example. A possible solution is to use a Content-Based Image Retrieval (CBIR) system, namely a system able to retrieve images from a database based on the similarity to a given reference image. The features used to describe the images strongly affect both the performance and the applicability of CBIR to medical images, motivating for the finding of a suitable set of feature for realizing an effective CBIR based PACS. In recent years, Deep Learning (DL) approaches outperformed classical machine learning methods in many computer vision applications, thanks to their ability to learn compact hierarchical features of input data that well fit the specific task to solve. In this paper we introduce a simple yet effective modular architecture to implement a “Smart PACS”, namely a PACS exploiting a deep-based CBIR compatible with the classical Hospital Information System (HIS) infrastructure. The feature extraction relies on Convolutional Neural Networks, a DL approach commonly applied in image processing, while the image indexing and look-up are based on Apache Solr. As application case-study, we analysed the need for a physician to obtain all the images of past studies having similar traits with the patient under analysis.