Liver lesion segmentation from abdomen computed tomography (CT) with deep neural networks remains challenging due to the small volume and the unclear boundary. To effectively tackle these problems, in this paper, we propose a cascaded deeply supervised convolutional networks (CDS-Net). The cascaded deep supervision (CDS) mechanism uses auxiliary losses to construct a cascaded segmentation method in a single network, focusing the network attention on pixels that are more difficult to classify, so that the network can segment the lesion more effectively. CDS mechanism can be easily integrated into standard CNN models and it helps to increase the model sensitivity and prediction accuracy. Based on CDS mechanism, we propose a cascaded deep supervised ResUNet, which is an end-to-end liver lesion segmentation network. We conduct experiments on LiTS and 3DIRCADb dataset. Our method has achieved competitive results compared with other state-of-the-art ones.

中文翻译：

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用于肝脏病变分割的级联深度监督卷积网络

由于体积小且边界不清楚，使用深度神经网络从腹部计算机断层扫描 (CT) 分割肝脏病变仍然具有挑战性。为了有效解决这些问题，在本文中，我们提出了一种级联深度监督卷积网络（CDS-Net）。级联深度监督（CDS）机制使用辅助损失在单个网络中构建级联分割方法，将网络注意力集中在更难分类的像素上，使网络能够更有效地分割病灶。CDS 机制可以很容易地集成到标准的 CNN 模型中，有助于提高模型的灵敏度和预测精度。基于CDS机制，我们提出了一个级联的深度监督ResUNet，它是一个端到端的肝脏病变分割网络。我们对 LiTS 和 3DIRCADb 数据集进行了实验。与其他最先进的方法相比，我们的方法取得了具有竞争力的结果。