Benchmark on automatic six-month-old infant brain segmentation algorithms: The iSeg-2017 challenge

Li Wang, Dong Nie, Guannan Li, Élodie Puybareau, Jose Dolz, Qian Zhang, Fan Wang, Jing Xia, Zhengwang Wu, Jia Wei 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. PluimChristian Desrosiers, Ismail Ben Ayed, Gerard Sanroma, Oualid M. Benkarim, Adrià Casamitjana, Verónica Vilaplana, Weili Lin, Gang Li, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

140 Citations (Scopus)


Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9months of age), due to inherentmyelination andmaturation process, WM and GM exhibit similar levels of intensity in both T1-weighted and T2-weighted MR images, making tissue segmentation very challenging. Although many efforts were devoted to brain segmentation, only a few studies have focused on the segmentation of six-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge ( provides a set of six-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 eight 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 the limitations and possible future directions. We hope the dataset in iSeg-2017, and this paper could provide insights into methodological development for the community.

Original languageEnglish
Article number2901712
Pages (from-to)2219-2230
Number of pages12
JournalIEEE Transactions on Medical Imaging
Issue number9
Publication statusPublished - 2019 Sept

Bibliographical note

Publisher Copyright:
© 2019 IEEE.


  • Brain
  • Challenge
  • Infant
  • Isointense phase
  • Segmentation

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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