TY - JOUR
T1 - Benchmark on automatic six-month-old infant brain segmentation algorithms
T2 - The iSeg-2017 challenge
AU - Wang, Li
AU - Nie, Dong
AU - Li, Guannan
AU - Puybareau, Élodie
AU - Dolz, Jose
AU - Zhang, Qian
AU - Wang, Fan
AU - Xia, Jing
AU - Wu, Zhengwang
AU - Chen, Jia Wei
AU - Thung, Kim Han
AU - Bui, Toan Duc
AU - Shin, Jitae
AU - Zeng, Guodong
AU - Zheng, Guoyan
AU - Fonov, Vladimir S.
AU - Doyle, Andrew
AU - Xu, Yongchao
AU - Moeskops, Pim
AU - Pluim, Josien P.W.
AU - Desrosiers, Christian
AU - Ayed, Ismail Ben
AU - Sanroma, Gerard
AU - Benkarim, Oualid M.
AU - Casamitjana, Adrià
AU - Vilaplana, Verónica
AU - Lin, Weili
AU - Li, Gang
AU - Shen, Dinggang
N1 - Funding Information:
Manuscript received December 20, 2018; revised February 17, 2019; accepted February 17, 2019. Date of publication February 27, 2019; date of current version August 30, 2019. This work was supported in part by the National Institutes of Health under Grant MH109773, Grant MH117943, Grant MH100217, Grant MH070890, Grant EB006733, Grant EB008374, Grant EB009634, Grant AG041721, Grant AG042599, Grant MH088520, Grant MH108914, Grant MH116225, and Grant MH107815. (Li Wang, Dong Nie, Guannan Li, Élodie Puybareau, Jose Dolz, Qian Zhang, Fan Wang, Jing Xia, Zhengwang Wu, Jia-Wei Chen, and Kim-Han Thung are co-first authors.) (Corresponding authors: Li Wang; Dinggang Shen.) L. Wang, D. Nie, G. Li, Q. Zhang, F. Wang, J. Xia, Z. Wu, J.-W. Chen, K.-H. Thung, W. Lin, and G. Li are with the Department of Radiology and the Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA (e-mail: li_wang@med.unc.edu).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - 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 (http://iseg2017.web.unc.edu) 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.
AB - 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 (http://iseg2017.web.unc.edu) 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.
KW - Brain
KW - Challenge
KW - Infant
KW - Isointense phase
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85085862576&partnerID=8YFLogxK
U2 - 10.1109/TMI.2019.2901712
DO - 10.1109/TMI.2019.2901712
M3 - Article
C2 - 30835215
AN - SCOPUS:85085862576
SN - 0278-0062
VL - 38
SP - 2219
EP - 2230
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 9
M1 - 2901712
ER -