Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge

Authors

Li Wang, Dong Nie, Guannan Li, Élodie Puybareau, Jose Dolz, Qian Zhang, Fan Wang, Jing Xia, Zhengwang Wu, Jiawei Chen, Kim-Han Thung, Toan Duc Bui, Jitae Shin, Guodong Zeng, Guoyan Zheng, Vladimir S Fonov, Andrew Doyle, Yongchao Xu, Pim Moeskops, Josien P W Pluim, Christian Desrosiers, Ismail Ben Ayed, Gerard Sanroma, Oualid M Benkarim, Adrià Casamitjana, Verónica Vilaplana, Weili Lin, Gang Li, Dinggang Shen

Journal

IEEE Transactions on Medical Imaging

Type

article

Projects

Olena

Date

2019-04-11

Abstract

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR imagesmaking tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.

Bibtex (lrde.bib)

@Article{	  wang.19.tmi,
  author	= {Li Wang and Dong Nie and Guannan Li and \'{E}lodie
		  Puybareau and Jose Dolz and Qian Zhang and Fan Wang and
		  Jing Xia and Zhengwang Wu and Jiawei Chen and Kim-Han Thung
		  and Toan Duc Bui and Jitae Shin and Guodong Zeng and Guoyan
		  Zheng and Vladimir S. Fonov and Andrew Doyle and Yongchao
		  Xu and Pim Moeskops and Josien P.W. Pluim and Christian
		  Desrosiers and Ismail Ben Ayed and Gerard Sanroma and
		  Oualid M. Benkarim and Adri\`{a} Casamitjana and
		  Ver\'{o}nica Vilaplana and Weili Lin and Gang Li and
		  Dinggang Shen},
  journal	= {IEEE Transactions on Medical Imaging},
  title		= {Benchmark on Automatic 6-month-old Infant Brain
		  Segmentation Algorithms: {T}he {iSeg}-2017 Challenge},
  year		= {2019},
  month		= sep,
  pages		= {2219--2230},
  volume	= {38},
  number	= {9},
  abstract	= {Accurate segmentation of infant brain magnetic resonance
		  (MR) images into white matter (WM), gray matter (GM), and
		  cerebrospinal fluid (CSF) is an indispensable foundation
		  for early studying of brain growth patterns and
		  morphological changes in neurodevelopmental disorders.
		  Nevertheless, in the isointense phase (approximately 6-9
		  months of age), due to inherent myelination and maturation
		  process, WM and GM exhibit similar levels of intensity in
		  both T1-weighted (T1w) and T2-weighted (T2w) MR images,
		  making tissue segmentation very challenging. Despite many
		  efforts were devoted to brain segmentation, only few
		  studies have focused on the segmentation of 6-month infant
		  brain images. With the idea of boosting methodological
		  development in the community, iSeg-2017 challenge
		  (http://iseg2017.web.unc.edu) provides a set of 6-month
		  infant subjects with manual labels for training and testing
		  the participating methods. Among the 21 automatic
		  segmentation methods participating in iSeg-2017, we review
		  the 8 top-ranked teams, in terms of Dice ratio, modified
		  Hausdorff distance and average surface distance, and
		  introduce their pipelines, implementations, as well as
		  source codes. We further discuss limitations and possible
		  future directions. We hope the dataset in iSeg-2017 and
		  this review article could provide insights into
		  methodological development for the community.},
  keywords	= {Image segmentation; Magnetic resonance imaging; Manuals;
		  Pediatrics; Biomedical imaging; Testing; White matter},
  doi		= {10.1109/TMI.2019.2901712}
}