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

Li Wang (Corresponding author), Dong Nie, Guannan Li, Elodie 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. PluimChristian Desrosiers, Ismail Ben Ayed, Gerard Sanroma, Oualid M. Benkarim, Adria Casamitjana, Veronica Vilaplana, Weili Lin, Gang Li, Dinggang Shen

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Scopus)
1 Downloads (Pure)

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.

Original languageEnglish
Article number2901712
Pages (from-to)2219-2230
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume38
Issue number9
DOIs
Publication statusPublished - 27 Feb 2019

Keywords

  • Brain
  • Challenge
  • Infant
  • Isointense phase
  • Segmentation

Fingerprint Dive into the research topics of 'Benchmark on automatic six-month-old infant brain segmentation algorithms: The iSeg-2017 Challenge'. Together they form a unique fingerprint.

  • Cite this

    Wang, L., Nie, D., Li, G., Puybareau, E., Dolz, J., Zhang, Q., Wang, F., Xia, J., Wu, Z., Chen, J., Thung, K-H., Bui, T. D., Shin, J., Zeng, G., Zheng, G., Fonov, V. S., Doyle, A., Xu, Y., Moeskops, P., ... Shen, D. (2019). Benchmark on automatic six-month-old infant brain segmentation algorithms: The iSeg-2017 Challenge. IEEE Transactions on Medical Imaging, 38(9), 2219-2230. [2901712]. https://doi.org/10.1109/TMI.2019.2901712