Label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. However, satisfying the requirements of higher accuracy and less running time is always a great challeng...Label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. However, satisfying the requirements of higher accuracy and less running time is always a great challenge. In this paper we propose a novel patch-based segmentation method combining a local weighted voting strategy with Bayesian inference. Multiple atlases are registered to a target image by an advanced normalization tools(ANTs) algorithm. To obtain a segmentation of the target, labels of the atlas images are propagated to the target image. We first adopt intensity prior and label prior as two key metrics when implementing the local weighted voting scheme, and then compute the two priors at the patch level. Further, we analyze the label fusion procedure concerning the image background and take the image background as an isolated label when estimating the label prior. Finally, by taking the Dice score as a criterion to quantitatively assess the accuracy of segmentations, we compare the results with those of other methods, including joint fusion, majority voting, local weighted voting, majority voting based on patch, and the widely used Free Surfer whole-brain segmentation tool. It can be clearly seen that the proposed algorithm provides better results than the other methods. During the experiments, we make explorations about the influence of different parameters(including patch size, patch area, and the number of training subjects) on segmentation accuracy.展开更多
评审专家遴选是技术评审中的关键环节。鉴于颠覆性技术专家预判平台预判系统对时效性和智能型的要求,专家遴选对预判结果具有决定性影响。通过学术专长匹配和专业遴选来选择符合要求的专家,可以降低成本,提高推荐效率与准确度,完成颠覆...评审专家遴选是技术评审中的关键环节。鉴于颠覆性技术专家预判平台预判系统对时效性和智能型的要求,专家遴选对预判结果具有决定性影响。通过学术专长匹配和专业遴选来选择符合要求的专家,可以降低成本,提高推荐效率与准确度,完成颠覆性技术的预测任务。基于学术网络表示学习的方法既可以避免大量特征工程,又可以方便不同类型的特征进行融合。利用异质网络表示学习方法和标签排序的学术专长画像方法构建专家库,并使用融合专家综合评价指标特征的匹配方法对待预判的颠覆性技术和专家专长进行匹配,为专家遴选提供一份专业背景匹配的候选专家列表。这种方法在Academic Social Network数据集上进行模拟实验。实验结果表明,这种方法能提升项目评审专家学术专长匹配,在加入综合指标特征后,专家的综合指标特征能有效地反馈到实验结果中,从而提高评审系统的时效性和智能性。展开更多
基金Project supported by the National Natural Science Foundation of China(No.61203224)the Science and Technology Innovation Foundation of Shanghai Municipal Education Commission,China(No.13YZ101)
文摘Label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. However, satisfying the requirements of higher accuracy and less running time is always a great challenge. In this paper we propose a novel patch-based segmentation method combining a local weighted voting strategy with Bayesian inference. Multiple atlases are registered to a target image by an advanced normalization tools(ANTs) algorithm. To obtain a segmentation of the target, labels of the atlas images are propagated to the target image. We first adopt intensity prior and label prior as two key metrics when implementing the local weighted voting scheme, and then compute the two priors at the patch level. Further, we analyze the label fusion procedure concerning the image background and take the image background as an isolated label when estimating the label prior. Finally, by taking the Dice score as a criterion to quantitatively assess the accuracy of segmentations, we compare the results with those of other methods, including joint fusion, majority voting, local weighted voting, majority voting based on patch, and the widely used Free Surfer whole-brain segmentation tool. It can be clearly seen that the proposed algorithm provides better results than the other methods. During the experiments, we make explorations about the influence of different parameters(including patch size, patch area, and the number of training subjects) on segmentation accuracy.
文摘评审专家遴选是技术评审中的关键环节。鉴于颠覆性技术专家预判平台预判系统对时效性和智能型的要求,专家遴选对预判结果具有决定性影响。通过学术专长匹配和专业遴选来选择符合要求的专家,可以降低成本,提高推荐效率与准确度,完成颠覆性技术的预测任务。基于学术网络表示学习的方法既可以避免大量特征工程,又可以方便不同类型的特征进行融合。利用异质网络表示学习方法和标签排序的学术专长画像方法构建专家库,并使用融合专家综合评价指标特征的匹配方法对待预判的颠覆性技术和专家专长进行匹配,为专家遴选提供一份专业背景匹配的候选专家列表。这种方法在Academic Social Network数据集上进行模拟实验。实验结果表明,这种方法能提升项目评审专家学术专长匹配,在加入综合指标特征后,专家的综合指标特征能有效地反馈到实验结果中,从而提高评审系统的时效性和智能性。
