Abstract
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.
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Acknowledgements
This work was supported by NIH (grant numbers R01-AG056014, R01-AG040271, P30-AG010124, R01-EB017255, R01-AG055005).
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Xie, L., Wang, J., Dong, M., Wolk, D.A., Yushkevich, P.A. (2019). Improving Multi-atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_39
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