U-Net及其变体实现多模态医学图像精准分割，提升诊断效率。
U-Net in Medical Image Segmentation: A Review of Its Applications Across Modalities
- 基于U-Net的深度学习模型自动完成像素级图像分割。
- 在MRI、CT、超声等多种成像模态中表现稳定可靠。
- 适合医疗影像分析研究人员及临床辅助诊断系统开发者。
医学影像在医疗中至关重要，可提供患者解剖与病理的关键信息，助力诊断与治疗。X射线、磁共振成像（MRI）、计算机断层扫描（CT）和超声（US）等非侵入性技术可捕捉器官、组织及异常的详细图像。有效分析这些图像需精确分割以划定感兴趣区域（ROI），如器官或病灶。传统分割方法依赖人工特征提取，耗时且结果因人而异。人工智能（AI）与深度学习（DL）的进展，尤其是卷积模型如U-Net及其变体（U-Net++、U-Net 3+），已显著改变医学图像分割（MIS），实现自动化与高精度。这些模型可在多种成像模态中高效完成像素级分类，克服了人工分割的局限。本文综述了各类医学影像技术，分析U-Net架构及其改进，讨论其在不同模态中的应用，并识别常见挑战，提出潜在解决方案。
原文摘要 · Abstract (English)
Medical imaging is essential in healthcare to provide key insights into patient anatomy and pathology, aiding in diagnosis and treatment. Non-invasive techniques such as X-ray, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US), capture detailed images of organs, tissues, and abnormalities. Effective analysis of these images requires precise segmentation to delineate regions of interest (ROI), such as organs or lesions. Traditional segmentation methods, relying on manual feature-extraction, are labor-intensive and vary across experts. Recent advancements in Artificial Intelligence (AI) and Deep Learning (DL), particularly convolutional models such as U-Net and its variants (U-Net++ and U-Net 3+), have transformed medical image segmentation (MIS) by automating the process and enhancing accuracy. These models enable efficient, precise pixel-wise classification across various imaging modalities, overcoming the limitations of manual segmentation. This review explores various medical imaging techniques, examines the U-Net architectures and their adaptations, and discusses their application across different modalities. It also identifies common challenges in MIS and proposes potential solutions.
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