Yang Minqiang, Zhang Yuhong, Chen Haoning, Wang Wei, Ni Haixu, Chen Xinlong, Li Zhuoheng, Mao Chengsheng
School of Information Science Engineering, Lanzhou University, Lanzhou, China.
School of Statistics and Data Science, Nankai University, Tianjin, China.
Front Oncol. 2022 Jun 2;12:894970. doi: 10.3389/fonc.2022.894970. eCollection 2022.
Image segmentation plays an essential role in medical imaging analysis such as tumor boundary extraction. Recently, deep learning techniques have dramatically improved performance for image segmentation. However, an important factor preventing deep neural networks from going further is the information loss during the information propagation process. In this article, we present AX-Unet, a deep learning framework incorporating a modified atrous spatial pyramid pooling module to learn the location information and to extract multi-level contextual information to reduce information loss during downsampling. We also introduce a special group convolution operation on the feature map at each level to achieve information decoupling between channels. In addition, we propose an explicit boundary-aware loss function to tackle the blurry boundary problem. We evaluate our model on two public Pancreas-CT datasets, NIH Pancreas-CT dataset, and the pancreas part in medical segmentation decathlon (MSD) medical dataset. The experimental results validate that our model can outperform the state-of-the-art methods in pancreas CT image segmentation. By comparing the extracted feature output of our model, we find that the pancreatic region of normal people and patients with pancreatic tumors shows significant differences. This could provide a promising and reliable way to assist physicians for the screening of pancreatic tumors.
图像分割在诸如肿瘤边界提取等医学成像分析中起着至关重要的作用。近年来，深度学习技术显著提高了图像分割的性能。然而，阻碍深度神经网络进一步发展的一个重要因素是信息传播过程中的信息损失。在本文中，我们提出了AX-Unet，这是一个深度学习框架，它结合了一个改进的空洞空间金字塔池化模块，用于学习位置信息并提取多级上下文信息，以减少下采样过程中的信息损失。我们还在每个层级的特征图上引入了一种特殊的分组卷积操作，以实现通道间的信息解耦。此外，我们提出一种显式的边界感知损失函数来解决边界模糊问题。我们在两个公开的胰腺CT数据集，即美国国立卫生研究院（NIH）胰腺CT数据集和医学分割十项全能（MSD）医学数据集中的胰腺部分上对我们的模型进行了评估。实验结果验证了我们的模型在胰腺CT图像分割中能够优于当前的先进方法。通过比较我们模型提取的特征输出，我们发现正常人和胰腺肿瘤患者的胰腺区域存在显著差异。这可以为辅助医生筛查胰腺肿瘤提供一种有前景且可靠的方法。
