Abstract
Accurate and consistent segmentation of infant brain MR images plays an important role in quantifying patterns of early brain development, especially in longitudinal studies. However, due to rapid maturation and myelination of brain tissues in the first year of life, the intensity contrast of gray and white matter undergoes dramatic changes. In fact, the contrast inverse around 6–8 months of age, when the white and gray matter tissues are isointense and hence exhibit the lowest contrast, posing significant challenges for segmentation algorithms. In this paper, we propose a longitudinally guided level set method to segment serial infant brain MR images acquired from 2 weeks up to 1.5 years of age, including the isointense images. At each single-time-point, the proposed method makes optimal use of T1, T2 and the diffusion-weighted images for complimentary tissue distribution information to address the difficulty caused by the low contrast. Moreover, longitudinally consistent term, which constrains the distance across the serial images within a biologically reasonable range, is employed to obtain temporally consistent segmentation results. Application of our method on 28 longitudinal infant subjects, each with 5 longitudinal scans, shows that the automated segmentations from the proposed method match the manual ground-truth with much higher Dice Ratios than other single-modality, single-time-point based methods and the longitudinal but voxel-wise based methods. The software of the proposed method is publicly available in NITRC (http://www.nitrc.org/projects/ibeat).
Highlights
The first year of life is the most dynamic phase of postnatal brain development
Most existing methods rely on the T2 modality for the neonates less than 3 months old [4,5,8] and T1 modality for the infants over 1-yearold [10,11], which demonstrates good contrast between white matter (WM) and gray matter (GM)
To validate our proposed method, we apply it to a group of 28 infants, each scanned at 5 time points: 2 weeks, 3, 6, 9, and 12 months
Summary
The first year of life is the most dynamic phase of postnatal brain development. Accurate tissue segmentation of infant brains in the first year of life has important implications for studying normal brain development, as well as for diagnose and treatment of neurodevelepmental disorders such as attention-deficit/hyperactivity disorder (ADHD), and autism. Most existing neonatal segmentation methods were proposed for single time-point image (less than 3 months). Most existing methods rely on the T2 modality for the neonates less than 3 months old [4,5,8] and T1 modality for the infants over 1-yearold [10,11], which demonstrates good contrast between white matter (WM) and gray matter (GM). At the middle of the first year (around 6–8 months of age), T2 and T1 modalities have lowest contrast where the WM and GM exhibit almost the same intensity level, which poses a significantly more challenging problem [12]
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