作为消化系统最常见的疾病之一,急性胰腺炎的医学影像仍使用简单的手工特征进行分析,效率与精度较低,与其危害性并不相称。由于胰腺的解剖变异性以及各种并发症,急性胰腺炎的影像表现复杂,不同患者不同种类的病灶差异大,基于CT影像的急...作为消化系统最常见的疾病之一,急性胰腺炎的医学影像仍使用简单的手工特征进行分析,效率与精度较低,与其危害性并不相称。由于胰腺的解剖变异性以及各种并发症,急性胰腺炎的影像表现复杂,不同患者不同种类的病灶差异大,基于CT影像的急性胰腺炎诊断难度较大。本文提出一种基于双重注意力网络用于诊断急性胰腺炎,该网络使用全局特征为不同阶段的局部特征生成局部注意力特征,使不同阶段的注意力特征关注不同尺度的病灶,最终通过融合对全局注意力特征进行分类。同时在生成注意力特征时,使用通道域注意力调整通道间的依赖,提高模型的表示能力。在真实的急性胰腺炎数据集上的实验结果表明,本文提出的网络取得了更好的急性胰腺炎诊断精度,相对原模型,灵敏度与曲线下面积(Area under the curve,AUC)分别至少提升了3.4%,3.2%;相较其他注意力机制如SENet对ResNet模型的改进,AUC提升2.7%。展开更多
Brain region-of-interesting (ROI) segmentation is an important prerequisite step for many computeraid brain disease analyses.However,the human brain has the complicated anatomical structure.Meanwhile,the brain MR imag...Brain region-of-interesting (ROI) segmentation is an important prerequisite step for many computeraid brain disease analyses.However,the human brain has the complicated anatomical structure.Meanwhile,the brain MR images often suffer from the low intensity contrast around the boundary of ROIs,large inter-subject variance and large inner-subject variance.To address these issues,many multi-atlas based segmentation methods are proposed for brain ROI segmentation in the last decade.In this paper,multi-atlas based methods for brain MR image segmentation were reviewed regarding several registration toolboxes which are widely used in the multi-atlas methods,conventional methods for label fusion,datasets that have been used for evaluating the multiatlas methods,as well as the applications of multi-atlas based segmentation in clinical researches.We propose that incorporating the anatomical prior into the end-to-end deep learning architectures for brain ROI segmentation is an important direction in the future.展开更多
文摘作为消化系统最常见的疾病之一,急性胰腺炎的医学影像仍使用简单的手工特征进行分析,效率与精度较低,与其危害性并不相称。由于胰腺的解剖变异性以及各种并发症,急性胰腺炎的影像表现复杂,不同患者不同种类的病灶差异大,基于CT影像的急性胰腺炎诊断难度较大。本文提出一种基于双重注意力网络用于诊断急性胰腺炎,该网络使用全局特征为不同阶段的局部特征生成局部注意力特征,使不同阶段的注意力特征关注不同尺度的病灶,最终通过融合对全局注意力特征进行分类。同时在生成注意力特征时,使用通道域注意力调整通道间的依赖,提高模型的表示能力。在真实的急性胰腺炎数据集上的实验结果表明,本文提出的网络取得了更好的急性胰腺炎诊断精度,相对原模型,灵敏度与曲线下面积(Area under the curve,AUC)分别至少提升了3.4%,3.2%;相较其他注意力机制如SENet对ResNet模型的改进,AUC提升2.7%。
基金Supported by the National Natural Science Foundation of China(Nos.61876082,61861130366,61703301)the Jiangsu Provincial 333 High-level Talent Cultivation Projects~~
文摘Brain region-of-interesting (ROI) segmentation is an important prerequisite step for many computeraid brain disease analyses.However,the human brain has the complicated anatomical structure.Meanwhile,the brain MR images often suffer from the low intensity contrast around the boundary of ROIs,large inter-subject variance and large inner-subject variance.To address these issues,many multi-atlas based segmentation methods are proposed for brain ROI segmentation in the last decade.In this paper,multi-atlas based methods for brain MR image segmentation were reviewed regarding several registration toolboxes which are widely used in the multi-atlas methods,conventional methods for label fusion,datasets that have been used for evaluating the multiatlas methods,as well as the applications of multi-atlas based segmentation in clinical researches.We propose that incorporating the anatomical prior into the end-to-end deep learning architectures for brain ROI segmentation is an important direction in the future.
