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Cascaded Deeply Supervised Convolutional Networks for Liver Lesion Segmentation
International Journal of Pattern Recognition and Artificial Intelligence ( IF 1.5 ) Pub Date : 2021-05-12
Kaiyi Peng, Bin Fang, Mingliang Zhou

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:

级联深度监督卷积网络用于肝脏病变分割

由于体积小且边界不清,利用深层神经网络从腹部计算机断层扫描(CT)进行肝脏病变分割仍然具有挑战性。为了有效解决这些问题,在本文中,我们提出了一个级联的深度监督卷积网络(CDS-Net)。级联深度监督(CDS)机制使用辅助损失在单个网络中构造级联分割方法,将网络注意力集中在难以分类的像素上,以便网络可以更有效地分割病变。CDS机制可以轻松地集成到标准CNN模型中,并且有助于提高模型的敏感性和预测准确性。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。我们在LiTS和3DIRCADb数据集上进行实验。与其他最新技术相比,我们的方法取得了竞争性结果。

更新日期:2021-05-17
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