Segmentation of Infant Hippocampus Using Common Feature Representations Learned for Multimodal Longitudinal Data
Aberrant development of the human brain during the first year after birth is known to cause critical implications in later stages of life. In particular, neuropsychiatric disorders, such as attention deficit hyperactivity disorder (ADHD), have been linked
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Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA 2 Department of Radiology, University of North Carolina at Chapel Hill, NC, USA [email protected]
Abstract. Aberrant development of the human brain during the first year after birth is known to cause critical implications in later stages of life. In particular, neuropsychiatric disorders, such as attention deficit hyperactivity disorder (ADHD), have been linked with abnormal early development of the hippocampus. Despite its known importance, studying the hippocampus in infant subjects is very challenging due to the significantly smaller brain size, dynamically varying image contrast, and large across-subject variation. In this paper, we present a novel method for effective hippocampus segmentation by using a multi-atlas approach that integrates the complementary multimodal information from longitudinal T1 and T2 MR images. In particular, considering the highly heterogeneous nature of the longitudinal data, we propose to learn their common feature representations by using hierarchical multi-set kernel canonical correlation analysis (CCA). Specifically, we will learn (1) within-time-point common features by projecting different modality features of each time point to its own modality-free common space, and (2) across-time-point common features by mapping all time-point-specific common features to a global common space for all time points. These final features are then employed in patch matching across different modalities and time points for hippocampus segmentation, via label propagation and fusion. Experimental results demonstrate the improved performance of our method over the state-of-the-art methods.
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Introduction
Effective automated segmentation of the hippocampus is highly desirable, as neuroscientists are actively seeking hippocampal imaging biomarkers for early detection of neurodevelopment disorders, such as autism and attention deficit hyperactivity disorder (ADHD) [1, 2]. Due to rapid maturation and myelination of brain tissues in the first year of life [3], the contrast between gray and white matter on T1 and T2 images undergo drastic changes, which poses great challenges to hippocampus segmentation. Multi-atlas approaches with patch-based label fusion have demonstrated effective performance for medical image segmentation [4-8]. This is mainly due to their ability *
Corresponding author.
© Springer International Publishing Switzerland 2015 N. Navab et al. (Eds.): MICCAI 2015, Part III, LNCS 9351, pp. 63–71, 2015. DOI: 10.1007/978-3-319-24574-4_8
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to account for inter-subjectt anatomical variation during segmentation. However, infant brain segmentation inttroduces new challenges that need extra consideration before multi-atlas segmentatiion can be applied. First, using either T1 or T2 imaages alone is insufficient to prov vide an effective tissue contrast for segmentation throuughout the first year. As shown n in Fig. 1, the T1 image has very poor tissue contrast between white matter (WM) and
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