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.

通过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，本研究评估了这些模型在各种挑战性医学图像分割任务中的表现，并通过解决图像标准化、调整大小、架构选择、损失函数设计和超参数调整等问题来获取结果。研究结果显示，经过深层网络层扩展的标准 UNet 是一种精湛的医学图像分割模型；而 Res-UNet 和 Attention Res-UNet 架构在处理精细图像细节时表现出更平滑的收敛和卓越的性能。此外，本研究还着重解决高类别不平衡问题，通过精心预处理和定义损失函数来处理。我们预期本研究结果将为希望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，并为其实施提供指导和最佳实践。