Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Machine learning and computer vision techniques have grown rapidly in recent years due to their automation, suitability, ability generate astounding results. Hence, this paper, we survey the key studies that are published between 2014 2022, showcasing different machine algorithms researchers used segment liver, hepatic tumors, hepatic-vasculature structures. We divide surveyed based on tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting tackle more than one task simultaneously. Additionally, classified as either supervised unsupervised, they further partitioned if amount work falls under a certain scheme is significant. Moreover, datasets challenges found literature websites containing masks aforementioned tissues thoroughly discussed, organizers’ original contributions those other researchers. Also, metrics excessively mentioned our review, stressing relevance at hand. Finally, critical future directions emphasized for innovative tackle, exposing gaps need addressing, such scarcity many vessels’ segmentation challenge why absence needs be dealt with sooner later.