Segmentation is a crucial step in microscopy image analysis. Numerous approaches have been developed over the past years, ranging from classical segmentation algorithms to advanced deep learning models. While U-Net remains one of the most popular and well-established models for biomedical segmentation tasks, recently developed transformer-based models promise to enhance the segmentation process of microscopy images. In this work, we assess the efficacy of transformers, including UNETR, the Segment Anything Model, and Swin-UPerNet, and compare them with the well-established U-Net model across various image modalities such as electron microscopy, brightfield, histopathology, and phase-contrast. Our evaluation identifies several limitations in the original Swin Transformer model, which we address through architectural modifications to optimise its performance. The results demonstrate that these modifications improve segmentation performance compared to the classical U-Net model and the unmodified Swin-UPerNet. This comparative analysis highlights the promise of transformer models for advancing biomedical image segmentation. It demonstrates that their efficiency and applicability can be improved with careful modifications, facilitating their future use in microscopy image analysis tools.

本研究针对显微镜图像分析中语义分割的关键问题，评估变换器模型（如UNETR、Segment Anything Model和Swin-UPerNet）与经典的U-Net模型在不同图像模式下的有效性。研究表明，通过对Swin Transformer模型进行结构改进，显著提升了分割性能，突显了变换器模型在生物医学图像分割中的先进潜力及其改进的可行性。

超越U-Net：评估视觉变换器在显微镜图像分析中的语义分割能力