Software Tools for Supporting Automatic Interpretation of Medical Images

Abstract

In the domain of medical imaging, the role of automated image interpretation tools is becoming increasingly critical in facilitating the diagnosis and treatment of diverse diseases. The escalating volume and intricacy of medical images necessitate the development of advanced tools that can support automatic image analysis. This paper outlines on-going work associated with the design and implementation of extensions and plugins for widely used DICOM viewers, specifically Weasis, Dicoogle, and Orthanc. The primary objective is to augment the functionality of these viewers, empowering them to assist radiologists and healthcare professionals in the comprehensive interpretation and analysis of medical images. This abstract outlines how DICOM viewer extensions and plugins can be integrated with machine learning models to enhance the efficiency and accuracy of medical image interpretation, ultimately leading to improved patient care and outcomes.