Benchmark on automatic six-month-old infant brain segmentation algorithms: The iSeg-2017 challenge
Benchmark on automatic six-month-old infant brain segmentation algorithms: The iSeg-2017 challenge
Wang, Li and Nie, Dong and Li, Guannan and Puybareau, Élodie and Dolz, Jose and Zhang, Qian and Wang, Fan and Xia, Jing and Wu, Zhengwang and Chen, Jia Wei and Thung, Kim Han and Bui, Toan Duc and Shin, Jitae and Zeng, Guodong and Zheng, Guoyan and Fonov, Vladimir S. and Doyle, Andrew and Xu, Yongchao and Moeskops, Pim and Pluim, Josien P.W. and Desrosiers, Christian and Ayed, Ismail Ben and Sanroma, Gerard and Benkarim, Oualid M. and Casamitjana, Adrià and Vilaplana, Verónica and Lin, Weili and Li, Gang and Shen, Dinggang
IEEE Transactions on Medical Imaging 2019
Abstract : 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.
