In this paper, we propose a method to enhance the performance of segmentation models for medical images. The method is based on convolutional neural networks that learn the global structure information, which corresponds to anatomical structures in medical images. Specifically, the proposed method is designed to learn the global boundary structures via an autoencoder and constrain a segmentation network through a loss function. In this manner, the segmentation model performs the prediction in the learned anatomical feature space. Unlike previous studies that considered anatomical priors by using a pre-trained autoencoder to train segmentation networks, we propose a single-stage approach in which the segmentation network and autoencoder are jointly learned. To verify the effectiveness of the proposed method, the segmentation performance is evaluated in terms of both the overlap and distance metrics on the lung area and spinal cord segmentation tasks. The experimental results demonstrate that the proposed method can enhance not only the segmentation performance but also the robustness against domain shifts.

中文翻译：

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利用全球结构信息来改善医学图像分割

在本文中，我们提出了一种增强医学图像分割模型性能的方法。该方法基于卷积神经网络，该卷积神经网络学习全局结构信息，该信息对应于医学图像中的解剖结构。具体而言，提出的方法旨在通过自动编码器学习全局边界结构，并通过损失函数约束分割网络。以这种方式，分割模型在学习的解剖特征空间中执行预测。与先前的研究通过使用预训练的自动编码器来训练分割网络来考虑解剖先验的研究不同，我们提出了一种结合学习分割网络和自动编码器的单阶段方法。为了验证所提出方法的有效性，根据肺区域的重叠度和距离度量以及脊髓分割任务来评估分割性能。实验结果表明，所提出的方法不仅可以增强分割性能，而且可以提高对域偏移的鲁棒性。