Yang Xiao, Han Xu, Park Eunbyung, Aylward Stephen, Kwitt Roland, Niethammer Marc
UNC Chapel Hill, Chapel Hill, USA.
Kitware, Inc., USA.
Simul Synth Med Imaging. 2016 Oct;9968:97-107. doi: 10.1007/978-3-319-46630-9_10. Epub 2016 Sep 23.
This paper proposes an approach to improve atlas-to-image registration accuracy with large pathologies. Instead of directly registering an atlas to a pathological image, the method learns a mapping from the pathological image to a quasi-normal image, for which more accurate registration is possible. Specifically, the method uses a deep variational convolutional encoder-decoder network to learn the mapping. Furthermore, the method estimates local mapping uncertainty through network inference statistics and uses those estimates to down-weight the image registration similarity measure in areas of high uncertainty. The performance of the method is quantified using synthetic brain tumor images and images from the brain tumor segmentation challenge (BRATS 2015).
本文提出了一种提高带有大病变的图谱到图像配准精度的方法。该方法不是直接将图谱配准到病理图像，而是学习从病理图像到准正常图像的映射，对于准正常图像可以进行更精确的配准。具体而言，该方法使用深度变分卷积编码器-解码器网络来学习这种映射。此外，该方法通过网络推理统计估计局部映射不确定性，并使用这些估计值在高不确定性区域降低图像配准相似性度量的权重。使用合成脑肿瘤图像和来自脑肿瘤分割挑战赛（BRATS 2015）的图像对该方法的性能进行了量化。
