Spot the Best Frame: Towards Intelligent Automated Selection of the Optimal Frame for Initialisation of Focal Liver Lesion Candidates in Contrast-Enhanced Ultrasound Video Sequences

Abstract

This paper describes a contribution to a wider project which aims to provide an intelligent automated assistant to radiologists performing the skilled and time-intensive task of detecting and characterising cancerous lesions within a human liver from Contrast-Enhanced Ultrasound (CEUS) video sequences. This particular contribution relates to automatically locating the optimal frame, for initialising a suspected focal liver lesion (FLL), within a CEUS video sequence. Currently, this task is routinely performed manually by radiologists, but is very time-consuming. The proposed approach is to use statistical and image processing techniques to automatically identify the most suitable frame for performing this initialisation, which should save the radiologist significant time and effort, bearingin mind the continuously increasing amount of CEUS data acquired and processed. In the future, this could be coupled with a method for automatically initialising the FLL's area within the area of the ultrasonographic image in this optimal frame and, together with already produced systems for the tracking and characterisation of such lesions, lead to a fully automated system assisting clinicians in the diagnosis of such lesions.