Abstract Accurate boundary segmentation in medical images is significant yet challenging due to large variation of shape, size and appearance within intra- and inter- samples. In this paper, we present a novel deep model termed as Crossover-Net for robust segmentation in medical images. The proposed model is inspired by an interesting observation – the features learned from horizontal and vertical directions can provide informative and complement contextual information to enhance discriminative ability between different tissues. Specifically, we first originally propose a cross-shaped patch, namely crossover-patch which consists of a pair of (orthogonal and overlapping) vertical and horizontal patches. Then, we develop our Crossover-Net to learn the vertical and horizontal crossover relation according to the proposed crossover-patches. To train our model end-to-end, we design a novel loss function to (1) impose the consistency on overlapping region of vertical and horizontal patches and (2) preserve the diversity on their non-overlapping regions. We have extensively evaluated our method on CT kidney tumor, MR cardiac, and X-ray breast mass segmentation tasks, showing promising results compared with the current state-of-the-art methods. The code is available at https://github.com/Qianyu1226/Crossover-Net .

中文翻译：

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Crossover-Net：利用垂直-水平交叉关系进行稳健的医学图像分割

摘要 由于样本内和样本间的形状、大小和外观的巨大变化，医学图像中的准确边界分割非常重要但具有挑战性。在本文中，我们提出了一种新的深度模型，称为 Crossover-Net，用于医学图像中的稳健分割。所提出的模型的灵感来自一个有趣的观察——从水平和垂直方向学习的特征可以提供信息和补充上下文信息，以增强不同组织之间的区分能力。具体来说，我们首先提出了一个十字形补丁，即交叉补丁，它由一对（正交和重叠）垂直和水平补丁组成。然后，我们开发我们的 Crossover-Net 以根据建议的交叉补丁学习垂直和水平交叉关系。为了端到端地训练我们的模型，我们设计了一个新的损失函数来（1）在垂直和水平补丁的重叠区域上强加一致性，以及（2）保持其非重叠区域的多样性。我们在 CT 肾肿瘤、MR 心脏和 X 射线乳腺肿块分割任务上广泛评估了我们的方法，与当前最先进的方法相比，显示出有希望的结果。代码可在 https://github.com/Qianyu1226/Crossover-Net 获得。与当前最先进的方法相比，显示出有希望的结果。代码可在 https://github.com/Qianyu1226/Crossover-Net 获得。与当前最先进的方法相比，显示出有希望的结果。代码可在 https://github.com/Qianyu1226/Crossover-Net 获得。