Segmentation of the liver from abdominal CT images is an essential step for computer-aided diagnosis and surgery planning. The U-Net architecture is one of the most well-known CNN architectures which achieved remarkable successes in both medical and biological image segmentation domain. However, it does not perform well when the target area is small or partitioned. In this paper, we propose a novel architecture, called dense feature selection U-Net (DFS U-Net), which addresses this challenging problem. Specifically, The Hounsfield unit values were windowed in a range to exclude irrelevant organs, and then use the pre-processed data to train our proposed DFS U-Net model. To further improve the segmentation accuracy of the small region and disconnected regions of interests with limited training datasets, we improve the loss function by adding a parameter to the formula. With respect to the ground truth, the Dice score ratio can reach over 94.9% for the liver. Our experimental results demonstrate its potential in clinical usage with high effectiveness, robustness and efficiency.

中文翻译：

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使用改进的卷积神经网络从腹部 CT 体积自动分割肝脏

从腹部 CT 图像分割肝脏是计算机辅助诊断和手术计划的重要步骤。U-Net 架构是最著名的 CNN 架构之一，在医学和生物图像分割领域都取得了显着的成功。但是，当目标区域较小或分区时，它表现不佳。在本文中，我们提出了一种新的架构，称为密集特征选择 U-Net (DFS U-Net)，它解决了这个具有挑战性的问题。具体来说，Hounsfield 单位值在一个范围内加窗以排除不相关的器官，然后使用预处理数据来训练我们提出的 DFS U-Net 模型。为了在训练数据集有限的情况下进一步提高小区域和不连续感兴趣区域的分割精度，我们通过在公式中添加一个参数来改进损失函数。就ground truth而言，肝脏的Dice得分比可以达到94.9%以上。我们的实验结果证明了其具有高效、稳健和高效的临床使用潜力。