ID 原文 译文
25155 同时本文算法具有较快的检测速度。 At the same time, the algorithm in this paper has a fast detection speed．
25156 当前基于深度学习的胰腺分割主要存在以下问题:(1)胰腺的解剖特异性导致深度网络模型容易受到复杂多变背景的干扰; Current deep learning-based pancreas segmentation mainly has the following problems: The anatomical specificity of the pancreas makes the deep network model easily disturbed by complex background;
25157 (2)传统两阶段分割方法在粗分割阶段将整张 CT 图像作为输入，导致依赖粗分割结果得到的定位不够准确; in the traditional two-stagesegmentation method, the input of the coarse segmentation is the entire CT image, which leads to inaccurate localization based on the segmentation results;
25158 (3)传统两阶段分割方法忽略了切片间的上下文信息，限制了定位和后续分割结果的提升。 the traditional two-stage segmentation ignores the context information between adjacent slices, which limits the localization and subsequent segmentation results．
25159 针对上述问题，本文提出了结合切片上下文信息的多阶段胰腺定位与分割方法。 In order to solve the problems above, a multi-stage pancreas localization and segmentation method combined with slices context information is proposed．
25160 第一阶段利用解剖先验定位粗略缩小输入区域; In the first stage, anatomical prior locating is used to roughly shrink the input area;
25161 第二阶段先使用所设计的 DASU-Net 进行粗略分割，接着利用切片上下文信息优化分割结果; in the second stage, the proposed DASU-Net is used for coarse segmentation, and then the segmentation results are optimized with slices context information;
25162 第三阶段使用单张切片定位进一步减少不相关背景，并使用 DASU-Net 完成精细分割。 last stage, single slice locating is used to further shrink irrelevant background, and then fine segmentation is completed by DASU-Net．
25163 实验结果表明，本文所提方法能够有效提高胰腺分割的准确率。 The experimental results show that the proposed method can effectively improve the accuracy of pancreas segmentation．
25164 针对基于深度神经网络模型的显著性检测方法中存在的模型训练困难、模型参数量大以及检测速度慢等问题，本文提出了一种融合小型深度生成模型的显著性检测方法。 Aiming at the difficulties of model training, large amount of model parameters, and slow detection speed in the saliency detection method based on deep neural network models, this paper proposes a saliency detection method that integrates small deep generative models．