IEEE/ACM Trans Comput Biol Bioinform. 2023 Nov-Dec;20(6):3399-3410. doi: 10.1109/TCBB.2022.3198425. Epub 2023 Dec 25.
Automatic liver tumor segmentation plays a key role in radiation therapy of hepatocellular carcinoma. In this paper, we propose a novel densely connected U-Net model with criss-cross attention (CC-DenseUNet) to segment liver tumors in computed tomography (CT) images. The dense interconnections in CC-DenseUNet ensure the maximum information flow between encoder layers when extracting intra-slice features of liver tumors. Moreover, the criss-cross attention is used in CC-DenseUNet to efficiently capture only the necessary and meaningful non-local contextual information of CT images containing liver tumors. We evaluated the proposed CC-DenseUNet on the LiTS dataset and the 3DIRCADb dataset. Experimental results show that the proposed method reaches the state-of-the-art performance for liver tumor segmentation. We further experimentally demonstrate the robustness of the proposed method on a clinical dataset comprising 20 CT volumes.
自动肝肿瘤分割在肝细胞癌的放射治疗中起着关键作用。在本文中，我们提出了一种新的基于密集连接的具有交叉注意力的 U-Net 模型（CC-DenseUNet），用于分割 CT 图像中的肝肿瘤。CC-DenseUNet 中的密集连接确保了在提取肝肿瘤的切片内特征时，编码器层之间的最大信息流。此外，交叉注意力用于 CC-DenseUNet 中，以有效地捕获包含肝肿瘤的 CT 图像中仅必要和有意义的非局部上下文信息。我们在 LiTS 数据集和 3DIRCADb 数据集上评估了所提出的 CC-DenseUNet。实验结果表明，所提出的方法在肝肿瘤分割方面达到了最先进的性能。我们进一步在包含 20 个 CT 容积的临床数据集上进行了实验，证明了所提出方法的稳健性。
