Zhang Tianyang, Yang Feiyang, Zhang Ping
The First Hospital of Jilin University, Changchun, Jilin, China.
College of Computer Science and Technology, Jilin University, Changchun, China.
Digit Health. 2024 Nov 3;10:20552076241293498. doi: 10.1177/20552076241293498. eCollection 2024 Jan-Dec.
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.
本文综述了深度学习在肝血管分割方面的进展及其在肝细胞癌（HCC）整体管理中的临床意义。HCC诊断和治疗的关键在于影像学检查，肝脏手术面临的挑战是对肝血管进行精确评估。在这方面，包括卷积神经网络在内的深度学习方法以及其他各种方法，显著提高了准确性和速度。该综述综合了30项研究的结果，涵盖网络架构、应用、监督技术、评估指标和动机等方面。此外，我们还探讨了深度学习技术在加强HCC综合诊断和治疗方面的挑战与未来前景，讨论了可能改变患者管理的预期突破。通过将临床需求与技术进步相结合，深度学习有望在肝血管分割领域取得更大突破，从而为HCC的诊断和治疗提供更有力的支持。
