UNet及其变体在医学图像分割中的性能分析
Performance Analysis of UNet and Variants for Medical Image Segmentation
AI总结：
本研究系统评估UNet及其变体在医学图像分割中的性能，通过优化预处理、损失函数与超参数，发现加深的标准UNet高效可用，而Res-UNet与Attention Res-UNet收敛更平稳且在细节处理上表现更优。
摘要：
医学成像在现代医疗中发挥着至关重要的作用，它通过非侵入性方式可视化内部结构和异常，从而实现疾病的早期检测、准确诊断和治疗规划。本研究旨在探索深度学习模型在医学图像分割中的应用，特别关注UNet架构及其变体。我们力求评估这些模型在各种具有挑战性的医学图像分割任务中的性能，解决图像归一化、尺寸调整、架构选择、损失函数设计和超参数调优等问题。研究结果表明，标准UNet在扩展深层网络层后是一种高效的医学图像分割模型，而Res-UNet和Attention Res-UNet架构则表现出更平滑的收敛性和更优越的性能，尤其是在处理精细图像细节时。本研究还通过仔细的预处理和损失函数定义来应对高度类别不平衡的挑战。我们预计本研究的结果将为寻求将这些模型应用于新的医学成像问题的研究人员提供有用的见解，并为其实现提供指导和最佳实践。
英文摘要：
Medical imaging plays a crucial role in modern healthcare by providing non-invasive visualisation of internal structures and abnormalities, enabling early disease detection, accurate diagnosis, and treatment planning. This study aims to explore the application of deep learning models, particularly focusing on the UNet architecture and its variants, in medical image segmentation. We seek to evaluate the performance of these models across various challenging medical image segmentation tasks, addressing issues such as image normalization, resizing, architecture choices, loss function design, and hyperparameter tuning. The findings reveal that the standard UNet, when extended with a deep network layer, is a proficient medical image segmentation model, while the Res-UNet and Attention Res-UNet architectures demonstrate smoother convergence and superior performance, particularly when handling fine image details. The study also addresses the challenge of high class imbalance through careful preprocessing and loss function definitions. We anticipate that the results of this study will provide useful insights for researchers seeking to apply these models to new medical imaging problems and offer guidance and best practices for their implementation.
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