Joint Segmentation and Registration for Infant Brain Images

被引:2
|
作者
Wu, Guorong [1 ,2 ]
Wang, Li [1 ,2 ]
Gilmore, John [3 ]
Lin, Weili [1 ,2 ]
Shen, Dinggang [1 ,2 ]
机构
[1] Univ N Carolina, Dept Radiol, Chapel Hill, NC 27599 USA
[2] Univ N Carolina, BRIC, Chapel Hill, NC 27599 USA
[3] Univ N Carolina, Dept Psychiat, Chapel Hill, NC 27599 USA
关键词
SHAPE;
D O I
10.1007/978-3-319-13972-2_2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately characterize structure changes is very critical in early brain development studies, which highly relies on the performance of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than the adult brains due to dynamic appearance change with rapid brain development. Fortunately, image segmentation and registration of infant images can assist each other to overcome the above difficulties by harnessing the growth trajectories (temporal correspondences) learned from a large set of training subjects with complete longitudinal data. To this end, we propose a joint segmentation and registration algorithm for infant brain images. 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 joint segmentation and registration method in early brain development studies.
引用
收藏
页码:13 / 21
页数:9
相关论文
共 50 条
  • [1] Scalable joint segmentation and registration framework for infant brain images
    Dong, Pei
    Wang, Li
    Lina, Weili
    Shen, Dinggang
    Wu, Guorong
    NEUROCOMPUTING, 2017, 229 : 54 - 62
  • [2] Joint Tumor Segmentation and Dense Deformable Registration of Brain MR Images
    Parisot, Sarah
    Duffau, Hugues
    Chemouny, Stephane
    Paragios, Nikos
    MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2012, PT II, 2012, 7511 : 651 - 658
  • [3] Consistent and Accurate Segmentation for Serial Infant Brain MR Images with Registration Assistance
    Sun, Yuhang
    Liu, Jiameng
    Liu, Feihong
    Sun, Kaicong
    Zhang, Han
    Shi, Feng
    Feng, Qianjin
    Shen, Dinggang
    MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2023, PT I, 2024, 14348 : 186 - 195
  • [4] Registration, segmentation, and visualization of multimodal brain images
    Viergever, MA
    Maintz, JBA
    Niessen, WJ
    Noordmans, HJ
    Pluim, JPW
    Stokking, R
    Vincken, KL
    COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2001, 25 (02) : 147 - 151
  • [5] MAP MRF joint segmentation and registration of medical images
    Wyatt, PP
    Noble, JA
    MEDICAL IMAGE ANALYSIS, 2003, 7 (04) : 539 - 552
  • [6] A variational joint segmentation and registration framework for multimodal images
    Ademaj, Adela
    Rada, Lavdie
    Ibrahim, Mazlinda
    Chen, Ke
    JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY, 2020, 14
  • [7] A Variational Joint Segmentation and Registration Framework for Multimodal Images
    Ademaj, Adela
    Rada, Lavdie
    Ibrahim, Mazlinda
    Chen, Ke
    MEDICAL IMAGE UNDERSTANDING AND ANALYSIS, MIUA 2019, 2020, 1065 : 305 - 316
  • [8] Deforming motion, shape average and the joint registration and segmentation of images
    Soatto, S
    Yezzi, AJ
    COMPUTER VISION - ECCV 2002 PT III, 2002, 2352 : 32 - 47
  • [9] AN MRF FRAMEWORK FOR JOINT REGISTRATION AND SEGMENTATION OF NATURAL AND PERFUSION IMAGES
    Mahapatra, Dwarikanath
    Sun, Ying
    2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, 2010, : 1709 - 1712
  • [10] A NOVEL FRAMEWORK FOR THE SEGMENTATION OF MR INFANT BRAIN IMAGES
    Mostapha, Mahmoud
    Casanova, Manuel F.
    El-Baz, Ayman
    2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2015, : 88 - 92