Neonatal brain image segmentation in longitudinal MRI studies
Introduction
Longitudinal MRI study in neonates has the potential to reveal the early brain development patterns (Dubois et al., 2008, Gerig et al., 2006, Knickmeyer et al., 2008). To perform quantitative analysis of longitudinal MR images in global and local brain regions, such as tissue volume measurement, inter-tissue boundary detection, and cortical surface and thickness evaluation, an essential procedure is to segment brain images into anatomically meaningful tissues such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The accuracy of tissue segmentation is crucial for subsequent quantitative analysis. However, despite the success of brain tissue segmentation algorithms developed for adult and pediatric brain images (Pham et al., 2000), it remains challenging to segment neonatal brain images (Mewes et al., 2006, Prastawa et al., 2005, Xue et al., 2007) because of poor spatial resolution, low tissue contrast, and high within-tissue intensity variability. As shown in Figs. 1a, b, in the neonatal MR T1 and T2 images, tissue intensity distributions of GM, WM, and CSF largely overlap, especially in the T1 image (Fig. 1a). Hindered by these difficulties, intensity-based segmentation algorithms, built upon an assumption that image intensities of the same brain tissue have a compact distribution, generally fail to segment neonatal brain images. Sometimes, it is difficult even for experts to visually distinguish between different neonatal brain tissues (Nishida et al., 2006, Song et al., 2007). However, it is worth noting that the quality of MRI brain images of one-year-old and two-year-old is much better as shown in Figs. 1c, d. This suggests that the image acquired at the late time-point is relatively easier to segment than the image acquired at neonatal stage. Thus, the segmentation result of the late time image can be potentially used to help the segmentation of neonatal brain image.
In the literature, the knowledge-based segmentation algorithms have been developed for neonatal brain image segmentation (Bazin and Pham, 2007, Nishida et al., 2006, Pham and Prince, 1999, Prastawa et al., 2005, Song et al., 2007, Warfield et al., 2000, Weisenfeld and Warfield, 2009, Xue et al., 2007). These algorithms perform brain tissue segmentation under the guidance of an atlas that encodes prior knowledge of anatomical structures' spatial locations, shapes, as well as their spatial relationships. A typical atlas generally comprises an intensity image, which can be used as an intensity model for atlas-to-subject registration, and also the probabilistic maps of GM, WM, and CSF, which can be used as prior tissue probability maps for guiding segmentation. The atlas can be generated by manually or automatically segmenting an individual image, or integrating information from multiple segmented images of different individual subjects.
For optimizing the performance of atlas-based segmentation on neonatal brain images, various atlases have been constructed. Due to high inter-subject anatomical variability, it is difficult for the atlas of an individual brain image to achieve a good segmentation performance, when applied to images with different anatomical structures. Researchers then turned to use the atlas built on multiple images. Prastawa et al. generated an atlas by averaging three semi-automatic segmented neonatal brain images, registered with affine transformation (Prastawa et al., 2005). Song et al. built an unbiased atlas from 9 of 10 neonates in a leave-one-out manner with diffeomorphic flow-based registration (Song et al., 2007). To minimize the age-related anatomical difference between images used for generating the atlas and the to-be-segmented image, Warfield et al. employed an age-specific atlas and proposed an iterated tissue segmentation and atlas alignment strategy to improve the neonatal tissue segmentation (Warfield et al., 2000). Xue et al. constructed multiple age-specific atlases and performed an expectation-maximization (EM) algorithm for tissue segmentation, in conjunction with Markov random field (MRF) to remove the mislabeled partial volume voxels in an iterative manner (Xue et al., 2007). Likewise, Aljabar et al. demonstrated that an atlas built from images similar to the to-be-segmented image can achieve better segmentation accuracy than atlases built from images randomly selected (Aljabar et al., 2009). These studies indicate that, for achieving good segmentation performance, it is necessary to have an atlas with similar anatomical structures with the to-be-segmented image, which we consider as a good atlas for guiding segmentation. On the other hand, the atlas built on multiple images can appear from blurry to sharp, depending on different settings of registration regularization in the atlas construction process, i.e., from strong to weak regularization (Yeo et al., 2008). The optimal atlas sharpness degree needs to be explored in order to maximize the guiding power of the atlas and achieve the best segmentation performance.
In atlas-based image segmentation algorithms, the segmentation performance is also affected by the registration procedure of registering the atlas to the to-be-segmented image. Different registration procedures typically yield different segmentation results even using the same atlas and the same subject image (Rohlfing et al., 2003). Therefore, an effective registration strategy is needed to estimate the spatial correspondence between the atlas and the to-be-segmented image for the accurate incorporation of the prior knowledge embedded in the atlas.
In longitudinal studies, a subject is scanned at multiple time-points. Thus, the images of the same subject acquired at different time-points have similar anatomical structures since major cortical gyrification develops during gestation of third trimester, and cortical convolution patterns remain similar after normal birth (Armstrong et al., 1995). The similarity of anatomical structures of the same subject at different development stages is observable in MRI images. As shown in Fig. 2, white matter pattern within the solid (and dashed) circles is very similar across images acquired at two weeks, one-year-, and two-year-old of the same subject. Since the segmentation of late time brain image such as two-year-old (or even one-year-old) can be achieved with high accuracy using existing segmentation methods like fuzzy clustering, we propose to use a late-time-point image in conjunction with its segmentation result as a subject-specific tissue probabilistic atlas to guide tissue segmentation of the neonatal image. Compared with the atlases built from images of different individuals, the subject-specific atlas has smaller anatomical variability, and thus has a potential to better guide tissue segmentation of the neonatal image. In particular, the subject-specific atlas can be used within a joint registration-segmentation framework to perform atlas alignment and image segmentation of the neonatal image.
This paper is organized as follows. Details of the proposed segmentation framework are presented in the Method section. Then, an experiment section presents the segmentation results produced by the proposed method, and the comparisons with manual delineations and two population-atlas-based segmentation methods. Finally, the paper is concluded in the Discussion section.
Section snippets
Method
In this paper, we propose to use a subject-specific atlas constructed from the image acquired at late time-point (e.g., one-year-old or two-year-old) of the same subject for guiding neonatal image segmentation. As summarized in Fig. 3, our algorithm consists of two components: (1) fuzzy segmentation of late time images and construction of tissue probabilistic atlas, as shown in the right panel of Fig. 3; and (2) joint registration-segmentation step for atlas alignment and atlas-based tissue
Materials and measurements
MRI images of neonates were obtained from a large dataset of longitudinal MR study, investigating brain development in early years. Currently, there are more than 180 subjects with longitudinal scans in neonate, one-year, and two-year-old. These healthy subjects were recruited from UNC-CH and free of congenital anomalies, metabolic disease, and focal lesions. MR scanning was performed using a 3 T Siemens scanner. T1 images with 160 axial slices were obtained with imaging parameters: TR = 1900 ms,
Discussion
We have presented a framework for performing neonatal brain tissue segmentation by using a subject-specific tissue probabilistic atlas generated from longitudinal data. This method takes the advantage of longitudinal imaging study in our project, i.e., using the segmentation results of the images acquired at a late time to guide the segmentation of the images acquired at neonatal stage. The experimental results demonstrate that the subject-specific atlas has superior performance, compared to
Acknowledgments
This work was supported in part by grants EB006733, EB008760, EB008374, EB009634, MH088520, NS055754, MH064065, and HD053000.
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