Progress and clinical translation in hepatocellular carcinoma of deep learning in hepatic vascular segmentation.

This paper reviews the advancements in deep learning for hepatic vascular segmentation and its clinical implications in the holistic management of hepatocellular carcinoma (HCC). The key to the diagnosis and treatment of HCC lies in imaging examinations, with the challenge in liver surgery being the precise assessment of Hepatic vasculature. In this regard, deep learning methods, including convolutional neural networksamong various other approaches, have significantly improved accuracy and speed. The review synthesizes findings from 30 studies, covering aspects such as network architectures, applications, supervision techniques, evaluation metrics, and motivations. Furthermore, we also examine the challenges and future prospects of deep learning technologies in enhancing the comprehensive diagnosis and treatment of HCC, discussing anticipated breakthroughs that could transform patient management. By combining clinical needs with technological advancements, deep learning is expected to make greater breakthroughs in the field of hepatic vascular segmentation, thereby providing stronger support for the diagnosis and treatment of HCC.