改进的具有对称旋转等变性质的U-Net血管分割方法
Improved Vessel Segmentation with Symmetric Rotation-Equivariant U-Net
AI总结：
针对现有CNN血管分割忽略旋转等变导致性能下降的问题，提出将高效对称旋转等变卷积核应用于U-Net，在减小模型规模的同时提升旋转图像分割性能，并超越现有等变方法。
摘要：
自动分割在医学图像分析和计算机辅助干预中发挥着关键作用。尽管基于卷积神经网络（CNN）的现有方法表现良好，但它们忽略了图像的有用等变性质，如旋转等变和反射等变。这一局限性会降低性能并导致预测不一致，尤其是在血管分割等缺乏明确方向性的应用中。虽然现有的等变学习方法试图缓解这些问题，但它们显著增加了学习成本、模型规模或两者兼有。为了克服这些挑战，我们提出了一种新颖的应用，将高效的对称旋转等变（SRE）卷积（SRE-Conv）核实现应用于U-Net架构，以学习旋转和反射等变特征，同时大幅减小模型规模。我们通过视网膜血管眼底成像上分割性能的提升验证了该方法的有效性。我们提出的SRE U-Net不仅在处理旋转图像时显著优于标准U-Net，而且以更少的可训练参数和更小的内存开销超越了现有的等变学习方法。代码可在https://github.com/OnofreyLab/sre_conv_segm_isbi2025获取。
英文摘要：
Automated segmentation plays a pivotal role in medical image analysis and computer-assisted interventions. Despite the promising performance of existing methods based on convolutional neural networks (CNNs), they neglect useful equivariant properties for images, such as rotational and reflection equivariance. This limitation can decrease performance and lead to inconsistent predictions, especially in applications like vessel segmentation where explicit orientation is absent. While existing equivariant learning approaches attempt to mitigate these issues, they substantially increase learning cost, model size, or both. To overcome these challenges, we propose a novel application of an efficient symmetric rotation-equivariant (SRE) convolutional (SRE-Conv) kernel implementation to the U-Net architecture, to learn rotation and reflection-equivariant features, while also reducing the model size dramatically. We validate the effectiveness of our method through improved segmentation performance on retina vessel fundus imaging. Our proposed SRE U-Net not only significantly surpasses standard U-Net in handling rotated images, but also outperforms existing equivariant learning methods and does so with a reduced number of trainable parameters and smaller memory cost. The code is available at https://github.com/OnofreyLab/sre_conv_segm_isbi2025.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
