Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting-猫眼课题宝

DOI：

10.1007/978-3-030-87193-2_7

发表时间：

2021-08

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Yue Zhang;Chengtao Peng;Liying Peng;Huimin Huang;Ruofeng Tong;Lanfen Lin;Jingsong Li;Yenwei Chen;Qingqing Chen;Hongjie Hu;Zhiyi Peng

多相计算机断层扫描（CT）图像为准确的肝脏肿瘤分割（LiTS）提供了重要的补充信息。最先进的多相LiTS方法通常通过相位加权求和或基于信道注意的连接来融合交叉相位特征。然而，这些方法忽略了不同阶段之间的空间（逐像素）关系，从而导致特征集成不足。此外，现有方法的性能仍然受到分割的不确定性的影响，这在肿瘤边界区域尤其严重。在这项工作中，我们提出了一种新的LiTS方法来充分聚合多相信息并改进不确定区域分割。为此，我们引入了空间聚合模块（SAM），该模块鼓励不同相位之间的逐像素交互，以充分利用跨相位信息。此外，我们设计了一个不确定区域绘制模块（URIM），利用相邻的判别特征来细化不确定像素。在内部多期CT局灶性肝脏病变数据集（mpct - fll）上的实验表明，我们的方法实现了有希望的肝脏肿瘤分割，并且优于目前的技术水平。

Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods usually fused cross-phase features through phase-weighted summation or channel-attention based concatenation. However, these methods ignored the spatial (pixel-wise) relationships between different phases, hence leading to insufficient feature integration. In addition, the performance of existing methods remains subject to the uncertainty in segmentation, which is particularly acute in tumor boundary regions. In this work, we propose a novel LiTS method to adequately aggregate multi-phase information and refine uncertain region segmentation. To this end, we introduce a spatial aggregation module (SAM), which encourages per-pixel interactions between different phases, to make full use of cross-phase information. Moreover, we devise an uncertain region inpainting module (URIM) to refine uncertain pixels using neighboring discriminative features. Experiments on an in-house multi-phase CT dataset of focal liver lesions (MPCT-FLLs) demonstrate that our method achieves promising liver tumor segmentation and outperforms state-of-the-arts.

Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting

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