Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges caused by various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architectures have been proposed and widely used, but their performance remains unsatisfactory. To further address these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations. (1) Since the discrimination of the features learned via square convolutional kernels needs to be further improved, we propose utilizing nonsquare vertical and horizontal convolutional kernels in a double-branch encoder so that the features learned by both branches can be expected to complement each other. (2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation of small-sized targets. With the above two schemes, we develop a novel double-branch encoder-based segmentation framework for medical image segmentation, namely, Crosslink-Net, and validate its effectiveness on five datasets with experiments. The code is released at https://github.com/Qianyu1226/Crosslink-Net.

中文翻译：

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Crosslink-Net：通过融合垂直和水平卷积进行医学图像分割的双分支编码器网络


精确的图像分割在医学图像分析中起着至关重要的作用，但它面临着形状多样、尺寸多样、边界模糊等带来的巨大挑战。为了解决这些困难，基于方核的编码器-解码器架构被提出并广泛使用，但其性能仍然不能令人满意。为了进一步解决这些挑战，我们提出了一种新颖的双分支编码器架构。我们的架构受到两个观察的启发。 （1）由于通过方形卷积核学习到的特征的辨别力需要进一步提高，我们建议在双分支编码器中使用非方形垂直和水平卷积核，以便两个分支学习的特征可以相互补充。 （2）考虑到空间注意力可以帮助模型更好地关注大尺寸图像中的目标区域，我们开发了注意力损失来进一步强调小尺寸目标的分割。通过上述两种方案，我们开发了一种新颖的基于双分支编码器的医学图像分割框架，即 Crosslink-Net，并通过实验在五个数据集上验证了其有效性。代码发布于https://github.com/Qianyu1226/Crosslink-Net。