Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer.

Objectives: Because radiotherapy is indispensible for treating cervical cancer, it is critical to accurately and efficiently delineate the radiation targets. We evaluated a deep learning (DL)-based auto-segmentation algorithm for automatic contouring of clinical target volumes (CTVs) in cervical cancers.

Methods: Computed tomography (CT) datasets from 535 cervical cancers treated with definitive or postoperative radiotherapy were collected.

PACS and Web-based image distribution and display.

Picture archiving and communication system (PACS) delivers images to the display workstations mostly through digital image communication in medicine (DICOM) protocols in radiology departments, and there are lots of medical applications in healthcare community needing to access PACS images for different application purposes. In this paper, we first reviewed a hospital-integrated PACS image data flow and typical diagnostic display software architecture, and discussed some Web technologies and Web-based image application server architectures, as well as image accessing and viewing methods in these architectures. Then, we present one approach to develop component-based image display architecture and use image processing and display component to build a diagnostic display workstation, and also, give a method to integrate this component into Web-based image distribution server to enable users using Web browsers to access, view and manipulate PACS DICOM images as easy as with PACS display workstations.