The overview of the deep learning integrated into the medical imaging of liver: a review

Abstract

Deep learning (DL) is a recently developed artificial intelligent method that can be integrated into numerous fields. For the imaging diagnosis of liver disease, several remarkable outcomes have been achieved with the application of DL currently. This advanced algorithm takes part in various sections of imaging processing such as liver segmentation, lesion delineation, disease classification, process optimization, etc. The DL optimized imaging diagnosis shows a broad prospect instead of the pathological biopsy for the advantages of convenience, safety, and inexpensiveness. In this paper, we reviewed the published representative DL-related hepatic imaging works, described the general situation of this new-rising technology in medical liver imaging and explored the future direction of DL development.

Keywords: Artificial intelligence; Computed tomography; Convolutional neural network; Deep learning; Image segmentation; Imaging diagnosis; Lesion classification; Liver disease; Magnetic resonance imaging; Ultrasonography.