Accurate segmentation of medical images is essential for clinical decision-making, and deep learning techniques have shown remarkable results in this area. However, existing segmentation models that combine transformer and convolutional neural networks often use skip connections in U-shaped networks, which may limit their ability to capture contextual information in medical images. To address this limitation, we propose a coordinated mobile and residual transformer UNet (MRC-TransUNet) that combines the strengths of transformer and UNet architectures. Our approach uses a lightweight MR-ViT to address the semantic gap and a reciprocal attention module to compensate for the potential loss of details. To better explore long-range contextual information, we use skip connections only in the first layer and add MR-ViT and RPA modules in the subsequent downsampling layers. In our study, we evaluated the effectiveness of our proposed method on three different medical image segmentation datasets, namely, breast, brain, and lung. Our proposed method outperformed state-of-the-art methods in terms of various evaluation metrics, including the Dice coefficient and Hausdorff distance. These results demonstrate that our proposed method can significantly improve the accuracy of medical image segmentation and has the potential for clinical applications.

Graphical Abstract

Illustration of the proposed MRC-TransUNet. For the input medical images, we first subject them to an intrinsic downsampling operation and then replace the original jump connection structure using MR-ViT. The output feature representations at different scales are fused by the RPA module. Finally, an upsampling operation is performed to fuse the features to restore them to the same resolution as the input image.

中文翻译：

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使用卷积神经网络和 Transformer 进行医学图像分割的新型深度学习模型

医学图像的精确分割对于临床决策至关重要，深度学习技术在该领域已显示出显着的效果。然而，结合变压器和卷积神经网络的现有分割模型通常在 U 形网络中使用跳跃连​​接，这可能会限制它们捕获医学图像中上下文信息的能力。为了解决这个限制，我们提出了一种协调移动和剩余变压器 UNet（MRC-TransUNet），它结合了变压器和 UNet 架构的优点。我们的方法使用轻量级 MR-ViT 来解决语义差距，并使用相互注意模块来补偿潜在的细节损失。为了更好地探索远程上下文信息，我们仅在第一层使用跳跃连​​接，并在后续下采样层中添加 MR-ViT 和 RPA 模块。在我们的研究中，我们评估了我们提出的方法在三个不同的医学图像分割数据集（即乳房、大脑和肺部）上的有效性。我们提出的方法在各种评估指标（包括 Dice 系数和 Hausdorff 距离）方面均优于最先进的方法。这些结果表明我们提出的方法可以显着提高医学图像分割的准确性，并具有临床应用的潜力。

图形概要

拟议 MRC-TransUNet 的图示。对于输入的医学图像，我们首先对其进行固有的下采样操作，然后使用 MR-ViT 替换原始的跳跃连接结构。不同尺度的输出特征表示由 RPA 模块融合。最后，执行上采样操作来融合特征，将它们恢复到与输入图像相同的分辨率。