Dataset Information

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

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.

SUBMITTER: Wang L

PROVIDER: S-EPMC6754324 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

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

Wang Li L Nie Dong D Li Guannan G Puybareau Elodie E Dolz Jose J Zhang Qian Q Wang Fan F Xia Jing J Wu Zhengwang Z Chen Jiawei J Thung Kim-Han KH Bui Toan Duc TD Shin Jitae J Zeng Guodong G Zheng Guoyan G Fonov Vladimir S VS Doyle Andrew A Xu Yongchao Y Moeskops Pim P Pluim Josien P W JPW Desrosiers Christian C Ayed Ismail Ben IB Sanroma Gerard G Benkarim Oualid M OM Casamitjana Adria A Vilaplana Veronica V Lin Weili W Li Gang G Shen Dinggang D

IEEE transactions on medical imaging 20190227

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 ima ...[more]

PMID: 30835215

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Project description:On?board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real?time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image?guided radiotherapy (MR?IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k?means (FKM), k?harmonic means (KHM), and reaction?diffusion level set evolution (RD?LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR?TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR?TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD?LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP?TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high?contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR?TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on?board MR?IGRT system. PACS number(s): 87.57.nm, 87.57.N?, 87.61.Tg

Dataset Information

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

Publications

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

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