Automatic liver and tumour segmentation in CT images are crucial in numerous clinical applications, such as postoperative assessment, surgical planning, and pathological diagnosis of hepatic diseases. However, there are still a considerable number of difficulties to overcome due to the fuzzy boundary, irregular shapes, and complex tissues of the liver. In this paper, for liver and tumor segmentation and to overcome the mentioned challenges a simple but powerful strategy is presented based on a cascade convolutional neural network. At the first, the input image is normalized using the Z-Score algorithm. This normalized image provides more information about the boundary of tumor and liver. Also, the Local Direction of Gradient (LDOG) which is a novel encoding algorithm is proposed to demonstrate some key features inside the image. The proposed encoding image is highly effective in recognizing the border of liver, even in the regions close to the touching organs. Then, a cascade CNN structure for extracting both local and semi-global features is used which utilized the original image and two other obtained images as the input data. Rather than using a complex deep CNN model with a lot of hyperparameters, we employ a simple but effective model to decrease the train and testing time. Our technique outperforms the state-of-the-art works in terms of segmentation accuracy and efficiency.

CT 图像中的自动肝脏和肿瘤分割在许多临床应用中至关重要，例如肝脏疾病的术后评估、手术计划和病理诊断。然而，由于肝脏边界模糊、形状不规则、组织复杂，仍有相当多的困难需要克服。在本文中，对于肝脏和肿瘤分割并克服上述挑战，提出了一种基于级联卷积神经网络的简单而强大的策略。首先，使用 Z-Score 算法对输入图像进行归一化。此标准化图像提供了有关肿瘤和肝脏边界的更多信息。此外，提出了一种新颖的编码算法局部梯度方向（LDOG）来演示图像内部的一些关键特征。所提出的编码图像在识别肝脏边界方面非常有效，即使在靠近接触器官的区域也是如此。然后，使用级联 CNN 结构提取局部和半全局特征，利用原始图像和另外两个获得的图像作为输入数据。我们没有使用具有大量超参数的复杂深度 CNN 模型，而是采用简单但有效的模型来减少训练和测试时间。我们的技术在分割精度和效率方面优于最先进的工作。我们没有使用具有大量超参数的复杂深度 CNN 模型，而是采用简单但有效的模型来减少训练和测试时间。我们的技术在分割精度和效率方面优于最先进的工作。我们没有使用具有大量超参数的复杂深度 CNN 模型，而是采用简单但有效的模型来减少训练和测试时间。我们的技术在分割精度和效率方面优于最先进的工作。