Improving Multi-atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation
Improving Multi-atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation
复制标题
通过基于卷积神经网络的补丁误差估计改进多图集分割
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Paul Yushkevich
中科院分区：
文献类型：
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作者：
L. Xie;Jiancong Wang;M. Dong;D. Wolk;Paul Yushkevich
多图谱分割被广泛应用于医学图像的自动标注。基于贴片的MAS方法的性能依赖于对局部贴片相似度的准确估计，该相似性是地图集贴片提供与目标贴片相同的标签的概率的代理。最近提出了基于学习的图像块嵌入技术，将原始强度转换为特征图，并与传统的原始强度或手工制作的特征相比，产生了有希望的改进。在这项研究中，我们提出了一种不同的方法，其中使用卷积神经网络(CNN)直接从斑块中估计出图谱斑块产生错误投票的概率，即具有与目标斑块不同的标签。实验表明，在T1加权MRI中，基于CNN的估计提高了常用的基于面片的MAS技术的分割精度，即空间变化加权投票和联合标签融合。
Multi-atlas segmentation (MAS) is widely used in automatically labeling medical images. The performance of patch-based MAS approaches relies on accurate estimation of local patch similarity, a proxy of the probability that an atlas patch provides the same label as the target patch. Learning-based image patch embedding techniques were recently proposed to transform raw intensity to feature maps and yield promising improvements compared to traditional raw intensity or hand-crafted features. In this study, we present a different approach in which the probability of atlas patch generating an erroneous vote, i.e. having a different label from the target patch, is directly estimated from the patches using a convolutional neural network (CNN). Experiments demonstrate that CNN-based estimates improve the segmentation accuracy of popular patch-based MAS techniques, i.e. spatially varying weighted voting and joint label fusion, in the context of segmenting medial temporal lobe subregions in T1-weighted MRI.