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

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Oncology

Background:

• Accurate assessment of hepatic vasculature is crucial for hepatocellular carcinoma (HCC) diagnosis and surgical planning.
• Current imaging techniques face challenges in precise hepatic vascular evaluation.
• Deep learning offers promising solutions to improve accuracy and speed in this domain.

Purpose of the Study:

• To review advancements in deep learning for hepatic vascular segmentation.
• To explore the clinical implications of these advancements in HCC management.
• To identify challenges and future prospects of deep learning in HCC diagnosis and treatment.

Main Methods:

• Systematic review of 30 studies on deep learning for hepatic vascular segmentation.
• Analysis of network architectures, applications, supervision techniques, and evaluation metrics.
• Synthesis of findings related to deep learning's impact on HCC management.

Main Results:

• Deep learning methods, particularly convolutional neural networks, have shown significant improvements in accuracy and speed for hepatic vascular segmentation.
• The reviewed studies demonstrate the potential of AI to enhance the precision of imaging examinations for HCC.
• Various deep learning approaches are being explored to address the complexities of liver vasculature.

Conclusions:

• Deep learning is poised to revolutionize hepatic vascular segmentation, offering stronger support for HCC diagnosis and treatment.
• Continued technological advancements combined with clinical needs will drive breakthroughs in AI-assisted HCC management.
• Future prospects include enhanced patient management through more precise and efficient diagnostic tools.