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Neurocomputing v.229, 2017년, pp.54 - 62   SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Scalable joint segmentation and registration framework for infant brain images

Dong, Pei (Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA ); Wang, Li ( Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA ); Lin, Weili ( Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA ); Shen, Dinggang ( Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA ); Wu, Guorong ( Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA );
  • 초록  

    Abstract The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study. Highlights We developed an efficient approach to deal with the tissue segmentation and registration for the infant brain MR images. Our proposed framework is scalable to various registration tasks in early brain development studies. Promising results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old. The proposed technique can be very useful for many ongoing early brain development studies.


  • 주제어

    Joint segmentation and registration .   Multi-atlas patch based label fusion .   Longitudinal growth trajectory .   Infant brain MR images.  

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