H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
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DOI：
10.1109/tmi.2018.2845918
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发表时间：
2018-12-01
影响因子：
10.6
通讯作者：
Heng, Pheng-Ann
中科院分区：
文献类型：
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作者：
Li, Xiaomeng;Chen, Hao;Heng, Pheng-Ann
肝癌是癌症死亡的主要原因之一。为了辅助医生进行肝癌的诊断和治疗计划，临床实践中迫切需要一种准确、自动的肝脏和肿瘤分割方法。最近，全卷积神经网络（FCN），包括2-D和3-D FCN，在许多体积图像分割中充当骨干。然而，2-D卷积不能完全利用沿第三维沿着的空间信息，而3-D卷积遭受高计算成本和GPU存储器消耗。为了解决这些问题，我们提出了一种新的混合密集连接的UNet（H-DenseUNet），它由一个2-D的DenseUNet有效地提取切片内的功能和3-D对应的分层聚合体积上下文的精神下，自动上下文算法的肝脏和肿瘤分割。我们以端到端的方式制定H-DenseUNet的学习过程，其中切片内表示和切片间特征可以通过混合特征融合层进行联合优化。我们在MICCAI 2017肝脏肿瘤分割挑战和3DIRCADb数据集的数据集上广泛评估了我们的方法。我们的方法在肿瘤的分割结果上优于其他最先进的方法，并且即使使用单个模型也可以实现非常有竞争力的肝脏分割性能。
Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-endmanner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.