Abstract
Accurate segmentation of liver is still challenging problem due to its large shape variability and unclear boundaries. The purpose of this paper is to propose a neural network based liver segmentation algorithm and evaluate its performance on abdominal CT images. First, we develop fully convolutional network (FCN) for volumetric image segmentation problem. To guide a neural network to accurately delineate target liver object, we apply self-supervising scheme with respect to edge and contour responses. Deeply supervising method is also applied to our low-level features for further combining discriminative features in the higher feature dimensions. We used 160 abdominal CT images for training and validation. Quantitative evaluation of our proposed network is presented with 8-fold cross validation. The result showed that our method successfully segmented liver more accurately than any other state-of-the-art methods without expanding or deepening the neural network. The proposed approach can be easily extended to other imaging protocols (e.g., MRI) or other target organ segmentation problems without any modifications of the framework.
Abstract (translated)
由于其大的形状可变性和不清楚的边界，准确的肝脏分割仍然是具有挑战性的问题。本文的目的是提出一种基于神经网络的肝脏分割算法，并评估其在腹部CT图像上的表现。首先，我们开发了完全卷积网络（FCN）用于体积图像分割问题。为了引导神经网络准确描绘目标肝脏对象，我们对边缘和轮廓响应应用自我监督方案。深度监督方法也适用于我们的低级特征，以进一步组合更高特征维度中的判别特征。我们使用160个腹部CT图像进行训练和验证。我们提出的网络的定量评估呈现8倍交叉验证。结果表明，我们的方法成功地比任何其他最先进的方法更准确地分割肝脏而不扩展或加深神经网络。所提出的方法可以容易地扩展到其他成像协议（例如，MRI）或其他目标器官分割问题，而无需对框架进行任何修改。
URL
https://arxiv.org/abs/1808.00739
