Department of Digestive Surgery, Hospital of Antibes Juan-les-Pins, Antibes, France.
Department of Digestive Surgery, University Hospital of Nice, Nice, France.
J Int Med Res. 2024 Sep;52(9):3000605241263170. doi: 10.1177/03000605241263170.
Liver vessel segmentation from routinely performed medical imaging is a useful tool for diagnosis, treatment planning and delivery, and prognosis evaluation for many diseases, particularly liver cancer. A precise representation of liver anatomy is crucial to define the extent of the disease and, when suitable, the consequent resective or ablative procedure, in order to guarantee a radical treatment without sacrificing an excessive volume of healthy liver. Once mainly performed manually, with notable cost in terms of time and human energies, vessel segmentation is currently realized through the application of artificial intelligence (AI), which has gained increased interest and development of the field. Many different AI-driven models adopted for this aim have been described and can be grouped into different categories: thresholding methods, edge- and region-based methods, model-based methods, and machine learning models. The latter includes neural network and deep learning models that now represent the principal algorithms exploited for vessel segmentation. The present narrative review describes how liver vessel segmentation can be realized through AI models, with a summary of model results in terms of accuracy, and an overview on the future progress of this topic.
从常规医学成像中对肝血管进行分割是一种有用的工具，可用于许多疾病的诊断、治疗计划和实施以及预后评估，尤其是肝癌。精确的肝解剖表示对于定义疾病的范围至关重要，在合适的情况下，可以进行切除或消融手术，以保证在不牺牲大量健康肝脏的情况下进行根治性治疗。血管分割曾经主要是手动完成的，耗费了大量的时间和人力，而目前则通过人工智能（AI）来实现，这使得该领域的兴趣和发展都有所增加。已经描述了许多用于实现这一目标的不同 AI 驱动模型，并可以分为不同的类别：阈值方法、基于边缘和区域的方法、基于模型的方法和机器学习模型。后者包括神经网络和深度学习模型，它们现在是用于血管分割的主要算法。本叙述性综述描述了如何通过 AI 模型实现肝血管分割，并总结了模型在准确性方面的结果，并概述了该主题的未来进展。
